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Mars' polar ice deposits are thought to preserve a record of climate throughout their evolution. In addition to the large north polar layered deposits (NPLD) at Mars' north pole, smaller ice deposits are preserved in craters nearby. These outlying deposits were potentially formed by the same mechanisms that drive NPLD formation, or may represent more local mechanisms. Distinguishing between these possibilities would help elucidate the spatial homogeneity of Martian climate processes. Here, we analyzed SHARAD radar depth profiles from 34 locations across the NPLD and 5 locations within the Korolev crater ice deposit using Fourier transform analysis and dynamic time warping to quantitatively assess the similarity between the internal layered stratigraphy of the two deposits. We identify broad stratigraphic similarities between the Korolev deposit and the NPLD, suggesting they likely formed due to the same climate forcing mechanism, with local variability also observed across the NPLD. Plain Language Summary: Mars has two large ice caps at its poles, which combined contain a similar volume of ice to Greenland on Earth. Near these large ice caps are craters that are also filled with ice, which may or may not have formed due to the same mechanisms that formed the large neighboring ice cap. Ground penetrating radar observations of ice on Mars can allow us to see layers of ice and dust that are present throughout the interior of these deposits, and represent climate events that have taken place during the deposit's formation and evolution. We analyze radar observations from two deposits near Mars' north pole and quantitatively show that these deposits have a similar pattern of layering. These results could indicate that they have shared a similar climate history, therefore implying the importance of regional‐scale climate processes on Mars in addition to local processes for forming these ice deposits. Key Points: We use Fourier transform analysis and dynamic time warping to assess the similarities between two ice deposits near Mars' north poleWe identified a periodic signal with an average wavelength of ∼45 m in radar observations of the ice mound in Korolev crater and the NPLDWe identify similar broad climate forcing for both Korolev and NPLD ice, with local variability across the NPLD also observed [ABSTRACT FROM AUTHOR]

The evolution of charge structure involved in lightning discharge of a thunderstorm over the central Tibetan Plateau is investigated for the first time, based on the data from very high frequency interferometer, radar and sounding. During the developing‐mature stage, the TP thunderstorm exhibited a tripolar charge structure evolved from an initial inverted dipole. At the mature stage, a bottom‐heavy tripole charge structure is clearly presented, with a strong lower positive charge center (LPCC) at temperatures above −10°C, a middle negative charge region between −30°C and −15°C, and an upper positive charge region at T < −30°C. As the LPCC was depleted, the charge structure evolved into a normal tripole with a pocket LPCC. The merging between different convective cells resulted in the formation of two adjacent negative charge regions located directly and obliquely above the LPCC, and horizontally arranged different charge regions were simultaneously involved in the same lightning discharge. Plain Language Summary: Tibetan Plateau thunderstorms usually exhibit special convective structures. Using the data from the accurate lightning VHF interferometer, electric field mill, fast/slow antenna and C‐band radar, the evolution of the charge structure of thunderstorms and their influence on lightning discharges are investigated. Our observation for the first time revealed the charge structure evolution of the central‐TP thunderstorm which involved in the lightning discharge, exhibiting a bottom heavy tripole charge structure with a large LPCC in the mature stage evolved from an initial inverted dipole and the usual tripole in the dissipating stage of the thunderstorm. Under different magnitudes of the LPCC, different types of lightning discharges including ‐IC, +IC and ‐CG flashes were generated, indicating the crucial effects of LPCC on the lightning discharge types. Key Points: The charge structure of the TP thunderstorm evolves from an initial inverted dipole to a mature stage tripole with a strong LPCCHorizontally distributed negative charge zones from cell merger are simultaneously involved in the discharge of a single lightning flashDifferences in the relative magnitude of LPCC leads to various types of lightning discharges [ABSTRACT FROM AUTHOR]

Monitoring the generation and movement of equatorial plasma bubbles (EPBs) in a large longitude region is crucial important for better understanding their day‐to‐day variability. Using the newly developed Low lAtitude long Range Ionospheric raDar (LARID) at Dongfang (19.2°N, 108.8°E, dip lat. 13.8°N), China, an extremely long‐range experiment for observing EPB irregularities in a range of ±9,600 km to the radar site was first carried out. The results show that EPB irregularities with ranges up to 7,000 and 9,500 km were observed by the east and west beams of LARID, respectively. By incorporating simultaneous observations from GNSS receiver and ionosonde networks, it is demonstrated that the EPBs generated from post‐sunset to sunrise over a very wide longitude of ∼140°, from Pacific to Africa could be observed by LARID. The results, for the first time, demonstrate the possibility for tracing global EPBs in real time using a few low latitude over‐the‐horizon radars. Plain Language Summary: Equatorial plasma bubble (EPB), which can cause severe ionospheric scintillation, is an important space weather phenomenon. The occurrence of EPBs exhibits complex longitude variation characteristics. Due to the fact that most of the equatorial and low latitude region is covered by ocean, it is challenging to monitor the generation and movement of global EPBs. Recently, an over‐the‐horizon (OTH) radar at low latitude, that is, the LARID, has been built for observing EPB irregularities. However, it is not clear that how far an OTH radar at low latitude can observe irregularities. This would be very important in the design of a low latitude OTH radar network for tracing global EPB irregularities. To address this issue, an extremely long‐range experiment covering a wide longitude of about 180° was performed for the first time with LARID. The successful observation of EPB irregularities from Pacific to Africa sectors demonstrates the possibility of monitoring the complex longitudinal variations of EPBs by an OTH radar, even during geomagnetic storms. The results provide meaningful insight for building a low latitude OTH radar network in future, that consists of three to four OTH radars could have the capability to obtain global EPBs in real time. Key Points: First extremely long‐range experiment for observing equatorial plasma bubbles over a large longitude was conductedEquatorial plasma bubbles with ranges as far as 9,500 km were successfully observed by an over‐the‐horizon radarThe results demonstrate the capability for tracing global equatorial plasma bubbles using a few low latitude over‐the‐horizon radars [ABSTRACT FROM AUTHOR]

Precise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low‐complexity and environment‐adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed‐form expression of the detection probability is derived. Simulations and on‐board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal‐to‐noise ratio ≥3 dB and false alarm probability = 10−8. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals. [ABSTRACT FROM AUTHOR]

Traditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value. [ABSTRACT FROM AUTHOR]

In tracking scenarios involving groups with dense targets, achieving effective data association is challenging due to mutual occlusion and interference among targets. The complexity of the tracking problem is further exacerbated in low‐observable environments by the increase in false alarm rates. The track‐before‐detect (TBD) is an advanced technology for detecting and tracking low‐observable targets, effectively mitigating data association problems by integrating multi‐frame echo data. However, the existing multi‐target TBD algorithms typically assume that the targets are spatially separated and are not suitable for scenarios involving group targets. A group target maximum‐likelihood probabilistic data association (GT‐ML‐PDA) algorithm, based on the concept of TBD, is proposed to track group targets effectively in low‐observable environments. The proposed algorithm divides group target tracking into two stages: group centre trajectory estimation and individual target trajectory estimation. To enhance the performance of the proposed algorithm, two strategies are suggested: modifying the equivalent measurements and extracting independent measurement sets for individual targets. Simulation results demonstrate that the proposed algorithm is capable of effectively tracking numerous individual targets within a group, even in the presence of heavy clutter. [ABSTRACT FROM AUTHOR]

Radar High‐Resolution Range Profile (HRRP) has great potential for target recognition because it can provide target structural information. Existing work commonly applies deep learning to extract deep features from HRRPs and achieve impressive recognition performance. However, most approaches are unable to distinguish between the target and non‐target regions in the feature extraction process and do not fully consider the impact of background noise, which is harmful to recognition, especially at low signal‐to‐noise ratios (SNR). To tackle these problems, the authors propose a radar HRRP target recognition framework termed Adaptive Soft Threshold Transformer (ASTT), which is composed of a patch embedding (PE) layer, ASTT blocks, and Discrete Wavelet Patch Merging (DWPM) layers. Given the limited semantic information of individual range cells, the PE layer integrates nearby isolated range cells into semantically explicit target structure patches. Thanks to its convolutional layer and attention mechanism, the ASTT blocks assign a weight to each patch to locate the target areas in the HRRP while capturing local features and constructing sequence correlations. Moreover, the ASTT block efficiently filters noise features in combination with a soft threshold function to further enhance the recognition performance at low SNR, where the threshold is adaptively determined. Utilising the reversibility of the discrete wavelet transform, the DWPM layer efficiently eliminates the loss of valuable information during the pooling process. Experiments based on simulated and measured datasets show that the proposed method has excellent target recognition performance, noise robustness, and small‐scale range shift robustness. [ABSTRACT FROM AUTHOR]

We propose a 10‐GHz 2 × 2 phased‐array radio frequency (RF) receiver with an 8‐bit linear phase and 15‐dB gain control range using 65‐nm complementary metal–oxide–semiconductor technology. An 8 × 8 phased‐array receiver module is implemented using 16 2 × 2 RF phased‐array integrated circuits. The receiver chip has four single‐to‐differential low‐noise amplifier and gain‐controlled phase‐shifter (GCPS) channels, four channel combiners, and a 50‐Ω driver. Using a novel complementary bias technique in a phase‐shifting core circuit and an equivalent resistance‐controlled resistor–inductor–capacitor load, the GCPS based on vector–sum structure increases the phase resolution with weighting‐factor controllability, enabling the vector–sum phase‐shifting circuit to require a low current and small area due to its small 1.2‐V supply. The 2 × 2 phased‐array RF receiver chip has a power gain of 21 dB per channel and a 5.7‐dB maximum single‐channel noise‐figure gain. The chip shows 8‐bit phase states with a 2.39° root mean‐square (RMS) phase error and a 0.4‐dB RMS gain error with a 15‐dB gain control range for a 2.5° RMS phase error over the 10 to10.5‐GHz band. [ABSTRACT FROM AUTHOR]

Subglacial lakes have been mapped across Antarctica with two methods, radio‐echo sounding (RES) and ice‐surface deformation. At sites where both are coincident, these methods typically provide conflicting interpretations about the ice‐bed interface. With a single exception, active subglacial lakes identified by surface deformation do not display the expected flat, bright, and specular bed reflection in RES data, characteristic of non‐active lakes. This observational conundrum suggests that our understanding of Antarctic subglacial hydrology, especially beneath important fast‐moving ice streams, remains incomplete. Here, we use an airborne RES campaign that surveyed a well‐characterized group of active subglacial lakes on lower Mercer and Whillans ice streams, West Antarctica, to explore inconsistency between the two observational techniques. We test hypotheses of increased scattering and attenuation due to the presence of an active subglacial lake system that could suppress reflected bed‐echo power for RES observations in these locations, finding that entrained water is most plausible. Plain Language Summary: The bottom of an ice sheet is insulated from cold air temperatures, often warm enough to melt and pond liquid water into lakes. These lakes beneath the ice sheet have been identified by two independent measurements, first with radar methods and second with changes in height of the ice surface (altimetry). Interestingly, the two methods rarely identify the same lakes: radar generally detects lakes in the ice‐sheet interior, whereas altimetry detects active lakes near the ice‐sheet margins that fill and drain within the time series of repeated measurements (∼years). In this study, we investigate a group of active subglacial lakes at which both radar and altimetry data sets are available. We demonstrate that the radar returns from active lake reflections are much dimmer than expected based on non‐active lake signatures and investigate the physical processes controlling those dim reflections. We argue that water moves into the ice when the lake fills or drains and that is the most plausible explanation for the observational discrepancy. Key Points: Active subglacial lakes, identified by surface deformation, do not create the expected bright and specular radar reflectionEntrained water in basal ice suppresses radar power by scattering and attenuation, and it also likely alters the basal ice mechanicsUnderstanding the radar expression of subglacial water on Earth provides context for investigations of subsurface water on planetary bodies [ABSTRACT FROM AUTHOR]

Estimates of radar attenuation in the shallow Martian subsurface are retrieved from RIMFAX soundings along the Perseverance rover traverse. Specifically, analyzed data is from the Hawksbill Gap area during the rover's first drives onto the Jezero Western Fan Front. The centroid frequency‐shift method is employed to quantify attenuation in terms of the constant‐Q approximation. Results are then compared with the amplitude decay method, which—in order to calculate attenuation—requires propagation velocities retrieved from radargram analysis. By verifying that results from two separate analyses are consistent, we ensure that quantified radar properties are well constrained. First estimate of constant‐Q is 78.8 ± 11.6. For a subsurface propagation velocity of 0.113 m/ns, that equals an attenuation of −2.1 ± 0.4 dB/m at the RIMFAX 675 MHz center frequency. Results are consistent with dry sedimentary rocks, and are distinguishable from the magmatic lithologies on Jezero Crater Floor. Plain Language Summary: This study presents first estimates of radar attenuation at the Jezero Western Fan Front. Measurements were made with the RIMFAX payload instrument on the Mars 2020 Perseverance rover mission, acquired along the rover drive path. Results indicate low signal losses in the subsurface that are consistent with dry sedimentary rocks, as observed on the surface by other payload instruments. Maximum imaging depths increase compared to imaging over magmatic lithologies on Jezero Crater Floor. By using separate methods of analysis (the centroid frequency‐shift method and the amplitude decay method), we reliably quantify attenuation and maximum penetration depths at the Western Fan Front, and observe differences to the Crater Floor lithologies. Key Points: The first estimate of radar attenuation at the Jezero Western Fan Front is on average −2.1 dB/mFor an average propagation velocity of 0.113 m/ns, returned power and time‐frequency analyses yield similar resultsRadar properties are consistent with dry sedimentary rocks and are distinguishable from the magmatic lithologies on Jezero Crater Floor [ABSTRACT FROM AUTHOR]

To ensure the ship navigation safety, it is necessary to detect the targets in the background of sea clutter around the ship. The most commonly used target detection equipments for ship navigation are marine radar and automatic identification system (AIS) device. However, marine radar echoes are often mixed with multiple clutter, and weak targets are not always equipped with AIS device, neither provides AIS information with low accuracy, leading to difficulty with target detection. Moreover, existing marine monitoring systems are accustomed to using the traditional information fusion methods for target detection, and the accuracy of identifying the weak targets is relatively low. To make full use of radar echo and AIS information and improve the accuracy of target detection, a Marine radar monitoring Internet of Things system is proposed, in which marine radars work in both scanning and staring modes. By adopting image segmentation and deep learning methods, the proposed design can enable accurate perception of weak target detection based on plan‐position indicator (PPI) images. A case study based on the selected X‐band radar datasets shows that the proposed design can achieve high target identification accuracy. [ABSTRACT FROM AUTHOR]

In this letter, a novel algorithm is proposed to design symbol‐level waveform for dual‐functional radar‐communication (DFRC) systems with low range sidelobe. Different from current schemes design waveform at the block level, a phase response constraint is introduced at the symbol level to provide an additional degree of freedom to decrease the range sidelobe in the pulse compression procedure, which is highly desired in radar systems. In particular, the phase response at the target direction is constrained to be similar to the given reference phase, and the matching error between the designed beampattern and the desired one is minimized subject to the constant modulus constraint. Furthermore, to guarantee the quality of service for communication, constructive interference is exploited at the symbol level for each communication user. An alternating direction method of multipliers algorithm is also proposed to solve the resulting nonconvex optimization problem with tractable subproblems solved sufficiently by the manifold optimization and standard quadratic problem. Numerical simulation results demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios. [ABSTRACT FROM AUTHOR]

The application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non‐linear filter problems, when prediction equations are continuous‐time and the update equations are discrete‐time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete‐time non‐linear filter problems, when prediction equations and update equations are discrete‐time. In order to assess the performance of the filters, the authors consider the non‐linear dynamics for a re‐entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re‐entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re‐entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins‐Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter. [ABSTRACT FROM AUTHOR]

Modern radar systems often face various interference signals in complex and rapidly changing electronic environments. The task of suppressing this interference in the radar echo signal to extract vital information is challenging. A radar interference suppression method is proposed based on a generative adversarial network (GAN). This method effectively recovers the target signal from the echo signal, which contains interference and noise, by leveraging the powerful fitting ability of GAN. Specifically, this method was tested using coherent suppression interference, smart noise interference, and noise frequency modulation suppression interference. We compared the proposed GAN method with recurrent neural network, short‐time Fourier transform time‐varying filtering, short‐time fractional Fourier transform time‐varying filtering algorithms and RNN approach. The results show that the interference suppression algorithm based on GAN is superior to the other three algorithms. [ABSTRACT FROM AUTHOR]

Beamforming is an effective way of resolving target direction and anti‐jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters' outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range‐Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam. [ABSTRACT FROM AUTHOR]

Forward‐looking radar imaging is a top priority due to a variety of applications. An airborne forward‐looking radar imaging approach via a modified propagator method (MPM) based on the planar phased array is proposed. It can obtain a better focusing effect without range migration correction. Through the MPM algorithm, the azimuth resolution can be enhanced greatly. Compared with the conventional synthetic aperture radar imaging algorithm, the left‐right ambiguity process can be avoided by generating two‐dimensional spatial spectra. Furthermore, the imaging results of different numbers of elements are given to provide an assessment of the imaging performance. Additionally, the authors proposed to apply the scanning planar phased array configuration to airborne forward‐looking two‐dimensional imaging for the first time. The validity of the method is proven by experiments. The image is more focused. Furthermore, the results of the experiments verify that the proposed method is fit for high‐speed situation and aircraft height variation condition. [ABSTRACT FROM AUTHOR]

Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)‐based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI‐based algorithms for NRT and provide results for its use. [ABSTRACT FROM AUTHOR]

Robustness against Electronic Warfare/Electronic Defence attacks represents an important advantage of Noise Radar Technology (NRT). An evaluation of the related Low Probability of Detection (LPD) and of Intercept (LPI) is presented for Continuous Emission Noise Radar (CE‐NR) waveforms with different operational parameters, that is, "tailored", and with various "degrees of randomness". In this frame, three different noise radar waveforms, a phase Noise (APCN) and two "tailored" noise waveforms (FMeth and COSPAR), are compared by time–frequency analysis. Using a correlator (i.e. a two antennas) receiver, assuming a complete knowledge of the band (B) and duration (T) of the coherent emission of these waveforms, it will be shown that the LPD features of a CE‐NR do not significantly differ from those of any CE radar transmitting deterministic waveforms. However, in real operations, B and T are unknown; hence, assuming an instantaneous bandwidth estimation will show that the duration T can be estimated only for some specific "tailored" waveforms (of course, not to be operationally used). The effect of "tailoring" is analysed with prospects for future work. Finally, some limitations in the classification of these radar signals are analysed. [ABSTRACT FROM AUTHOR]

A novel method is proposed for rail fastener detection based on millimetre‐wave (mmWave) radar, mmWave radar fastener counter (MMW‐FC), which can accurately detect and record the fasteners in real‐time as the train traverses its route. Under circumstances where GNSS signals remain unavailable for prolonged durations, precise train localisation can be accomplished by correlating the number of fasteners derived from this method with the corresponding track map. Initially, MMW‐FC utilises fast Fourier transform and adaptive beamforming to focus the energy reflected from fasteners. Subsequently, it applies an adaptive template‐matching algorithm to detect each fastener. Furthermore, by leveraging known fastener spacing and the average time for trains to pass adjacent fasteners, the Kalman filter can execute precise speed tracking, used as a speed reference when adjusting the matching template adaptively. The experimental results indicate that the proposed method can precisely count the fasteners the train encounters in diverse road and speed conditions. The fastener counter maintains the Counting Error less than 0.067%, the speed error stays below 1.8 km/h, and the maximum values of the mean absolute error and root mean square error for speed are 0.7337 and 0.9584 km/h, respectively. [ABSTRACT FROM AUTHOR]

The utilisation of continuous random waveforms for radar, that is, noise radar, has been extensively studied as a candidate for low probability of intercept operation. However, compared with the more traditional pulse‐Doppler radar, noise radar systems are significantly more complicated to implement, which is likely why few systems exist. If noise radar systems are to see the light of day, system design, implementation, limitations etc., must be investigated. Therefore, the authors examine and detail the implementation of a real‐time noise radar system on a field programmable gate array. The system is capable of operating with 100% duty cycle, 200 MHz bandwidth, and 268 ms integration time while processing a range of about 8.5 km. Additionally, the system can perform real‐time moving target compensation to reduce cell migration. System performance is primarily limited by the memory bandwidth of the off‐chip dynamic random access memory. [ABSTRACT FROM AUTHOR]

A THz radar, with its wide bandwidth, is capable of high‐resolution imaging down to the centimeter scale. In this study, a THz radar is applied to detect hydrometeors generated in a spray chamber. The observed backscattering signals show fluctuations at centimeter scales, indicating various hydrometeor distribution patterns along the radar beam. A co‐located High‐Speed Imaging (HSI) sensor is used to measure the Drop Size Distributions (DSD) in the spray chamber. The radar sampling beam is well aligned with the HSI probes, allowing an objective comparison between the remote sensing and in situ observations. In this study, the observed radar power is compared with the power estimated from the HSI measurements. Results show great consistency, with power difference smaller than 0.5 dB. This study demonstrates the feasibility and great potential of using a THz radar for ultra‐high‐resolution observations of clouds in a laboratory facility, and in the real atmosphere. Plain Language Summary: An improved understanding of how precipitation forms in clouds calls for new observational instruments and novel measurement strategies. Radars operating at ultra‐high frequency (a.k.a. THz radars) are known for their unprecedented resolution down to centimeter scale. Here, for the first time, we applied a THz radar to detect hydrometeors generated in a spray chamber. An additional in situ probe is utilized within the chamber to validate the THz radar observations. The radar beam is directed through the same volume that is sampled by the in situ probe, thus enabling an objective comparison between the in situ and remote sensing measurements. Different hydrometeor distributions are generated in the chamber, each being jointly observed by the radar and the in situ probes. Results show that the THz radar measurements agree well with the radar power calculated from the in situ observations. This unique experiment indicates promising scenarios of applying THz radar to cloud and precipitation studies in a laboratory facility and in the real atmosphere. Key Points: A novel experiment is conducted by applying a THz radar to detect hydrometeors generated in a spray chamberGood agreement between in situ and radar observations demonstrates the unique capability of THz radar for cloud and precipitation studiesImplications of applying THz radar to design novel observational strategies for solving fundamental gaps in cloud physics are demonstrated [ABSTRACT FROM AUTHOR]

Tornadoes, as highly destructive weather events, require accurate detection for effective decision‐making. Traditional radar‐based tornado detection algorithms (TDA) face challenges with limited tornado feature extraction capabilities, leading to high false alarm rates and low detection probabilities. This study introduces the Multi‐Task Identification Network (MTI‐Net), leveraging Doppler radar data to enhance tornado recognition. MTI‐Net integrates tornado detection and estimation tasks to acquire comprehensive spatial and locational information. As part of MTI‐Net, we introduce a novel backbone network of Multi‐Head Convolutional Block (MHCB), which incorporates Spatial and Channel Attention Units (SAU and CAU). SAU optimizes local tornado feature extraction, while CAU reduces false alarms by enhancing dependencies among input variables. Experiments demonstrate the superiority of MTI‐Net over TDA, with a decrease in false alarm rates from 0.94 to 0.46 and an increase in hit rates from 0.23 to 0.81, highlighting the effectiveness of MTI‐Net in handling small‐scale tornado events. Plain Language Summary: Tornadoes, highly destructive small‐scale weather phenomena, demand accurate detection for informed decision‐making. Although meteorological radars are commonly utilized for tornado identification, current methods often suffer from false alarms or missed detections due to radar noise. In this study, we introduce the multi‐task learning‐based identification network (MTI‐Net), which not only enables tornado detection but also estimates tornado counts within radar data. We integrate Convolutional Neural Networks (CNNs) with Transformer techniques to enhance the model's ability to capture tornado information. CNNs detect local details using filters, while Transformers manage global connections through attention mechanisms. A series of experiments demonstrate significant improvements in tornado detection with MTI‐Net compared to traditional methods. Key Points: A tornado dataset with detailed radar features was created over China from 2017 to 2023Multi‐task learning was designed to simultaneously infer tornado detection and tornado number estimationIntegration of spatial and channel attention units can better extract tornado features from radar data [ABSTRACT FROM AUTHOR]

Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities. Plain Language Summary: Ground radars can provide continuous spatial observations over large areas with high spatiotemporal resolutions, so they form the infrastructure for precipitation monitoring and observation in many countries. Recently, deep learning (DL) techniques have shown great potential for use in polarimetric radar‐based precipitation estimates. Nevertheless, the black‐box and turn‐key characteristics of DL models make it difficult for researchers to understand the model decision‐making process and cast doubt on the reliability of the model results. This study introduces a physically explainable polarization radar‐based quantitative precipitation estimation (QPE) system built on DL technology that can explain the causes of the precipitation estimates provided by deep learning models under different rainfall amounts. An experiment indicates that our model achieves better estimates than the conventional methods. Furthermore, the explainability methodology allows for visualization of the microphysical precipitation information. Being the initial attempt to apply explainability learning in the QPE domain, the explainability results may offer valuable guidance for rainfall estimation. Key Points: A polarimetric radar‐based rainfall estimation system is developed using deep neural networksThe deep learning‐based rainfall estimates generally outperform products derived from traditional parametric relationsThe proposed deep learning interpretation method can provide physical and statistical explanations of the model decision‐making process [ABSTRACT FROM AUTHOR]

Seabirds interact with fishing vessels to consume fishing discards and baits, sometimes resulting in incidental capture (bycatch) and the death of the bird, which has clear conservation implications. To understand seabird–fishery interactions at large spatiotemporal scales, researchers are increasing their use of simultaneous seabird and fishing vessel tracking. However, vessel tracking data can contain gaps due to technical problems, illicit manipulation, or lack of adoption of tracking monitoring systems. These gaps might lead to underestimating the fishing effort and bycatch rates and jeopardize the effectiveness of marine conservation. We deployed bird‐borne radar detector tags capable of recording radar signals from vessels. We placed tags on 88 shearwaters (Calonectris diomedea, Calonectris borealis, and Calonectris edwardsii) that forage in the northwestern Mediterranean Sea and the Canary Current Large Marine Ecosystem. We modeled vessel radar detections registered by the tags in relation to gridded automatic identification system (AIS) vessel tracking data to examine the spatiotemporal dynamics of seabird–vessel interactions and identify unreported fishing activity areas. Our models showed a moderate fit (area under the curve >0.7) to vessel tracking data, indicating a strong association of shearwaters to fishing vessels in major fishing grounds. Although in high‐marine‐traffic regions, radar detections were also driven by nonfishing vessels. The tags registered the presence of potential unregulated and unreported fishing vessels in West African waters, where merchant shipping is unusual but fishing activity is intense. Overall, bird‐borne radar detectors showed areas and periods when the association of seabirds with legal and illegal fishing vessels was high. Bird‐borne radar detectors could improve the focus of conservation efforts. [ABSTRACT FROM AUTHOR]

The frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross‐regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U‐shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

In electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open‐set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi‐RNN). RBi‐RNN utilises input reconstruction and employs a joint training strategy incorporating cross‐entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter‐class latent representation distances while minimising intra‐class disparities. By incorporating an open‐set recognition method based on extreme value theory, RBi‐RNN adapts to open‐set scenarios. Simulation results demonstrate the superiority of RBi‐RNN over conventional models in both closed‐set and open‐set scenarios. In open‐set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses. [ABSTRACT FROM AUTHOR]

Two‐dimensional direction‐of‐arrival (DoA) estimation in azimuth and elevation via radar systems equipped with uniform rectangular arrays (URAs) will play an important role in various application areas—most distinctively in future urban air mobility settings with unmanned aerial vehicles. A key factor is the fast and reliable provision of target detections in terms of range and DoA for safe autonomous operation of the vehicle using on‐board antenna arrays with compact installation size. The authors present a technique for improving the performance of DoA estimation using compressive sensing in conjunction with multiple‐input multiple‐output arrays with electronically steered beams in the transmit direction. The simulation study investigates the impact of different design considerations on radar signal processing performance. An optimisation of a radar system using electronic beamsteering in the transmit domain is presented numerically. Based on the architecture of the URAs used, performance and detection accuracy can be improved. [ABSTRACT FROM AUTHOR]

Nowadays, modern radar systems increase their target detection capabilities by processing pulses coherently. On the other hand, digital radio frequency memory‐based modern jammers have the ability to work coherently and can deceive radars even with a very low effective radiated power. These jammers, which have the capability of storing the radar's pulse, can use the previous pulses that they have stored in their memory during the electronic deception, without waiting for the last pulse of the radar, in other words, before the new pulse is received. If the radar does not change its parameters from pulse to pulse, such smart jamming techniques applied in this way can be very effective. In this article, the authors propose to use a smart binary phase‐coding method for pulse compression radar as an electronic protection technique against repeater jamming. This approach further improves the target detection capability of modern radar systems, which use coherent integration in the receiver. The proposed method can provide high protection against digital radio frequency memory‐based repetitive range deception techniques without compromising the radar's target detection capability. In the simulations, the traditional approach in which the same code is used without changing from pulse‐to‐pulse, and the approach using code sets obtained by the smart binary phase‐coding method in intra‐pulse modulation are compared. The results show that the proposed method can significantly improve the isolation against deception jamming and the target detection capability simultaneously. [ABSTRACT FROM AUTHOR]

The Alps are a natural barrier for avian broad‐front migration in Central Europe. While most birds that approach the Alps are deflected and circumvent the mountains, some choose to make the crossing. Here, they are funnelled and channelled in valleys, leading to high bird densities. Many Alpine valleys are suitable locations for wind farms, potentially creating a conflict between wind energy production and bird conservation. Collisions can be reduced by temporarily shutting down wind turbines. This however requires timely coordination, either by locally monitoring migration intensity or by extrapolating and forecasting migratory fluxes from other sites. However, little is known about the timing and intensity of bird migration in valleys of the central Alps, especially during spring migration. This study presents a 2‐year quantification of avian migration across the Alps. We collected terrestrial radar data at three sites: two located in Alpine valleys and one in the lowland, close to the northern foothills of the Alps. We found high migration traffic rates (MTR) during both migration seasons in the Alpine valleys, with outstanding numbers of migrants during the spring season. The strong alignment of the flight directions with the main orientation of alpine valleys highlights the importance of valleys and the connected passes in channelling migratory fluxes through the Alps. However, extrapolating migration intensities and forecasting peak migration events for inner Alpine sites is difficult, likely due to how migratory patterns and activity are influenced by the complexity of the local topography and the associated dynamic wind and weather conditions. Instead, we call for year‐round on‐site monitoring of migration intensities and strategies tailored to the local context to reduce the risk of bird strikes at wind turbines in the Alps. [ABSTRACT FROM AUTHOR]

Previous theoretical simulations showed the generation of double‐layer structures of E‐region field‐aligned irregularities (FAIs). In this study, we report the double‐layer structures of E‐region FAIs observed by an all‐sky radar at low latitude Ledong (18.4°N, 109°E), China. These FAIs appeared at the altitudes ∼90 and 110 km respectively. Both layers displayed quasi‐periodic patterns with synchronized spatial features, that is, being manifested as spatially separated patches or wavelike structures over similar longitudes at the two layers simultaneously. The neutral wind observations revealed the existence of wind shears at two separated altitudes where the double FAI layers occurred. The two wind shears created two vertically separated sporadic E layers and possibly drove the generation of the double‐layer FAIs via gradient drift instability. The synchronized spatial features of the FAIs at the two layers could be modulated by gravity waves. Plain Language Summary: The E‐region field‐aligned irregularities (FAIs) were observed usually in a form of single layer. Due to the limited field of view of narrow‐beam radars which were traditionally employed for observing ionospheric irregularity, the spatial morphology and evolution of E‐region FAIs in a small region were resolved. Owing to the recently developed capability of all‐sky radars in observing E‐region irregularities, the spatial features of E‐region irregularities over a larger zonal region up to hundreds of kilometers in the horizontal plane can be derived. Further, the spatial features of E‐region irregularities in the vertical direction can be unambiguously determined. Utilizing an all‐sky radar at low latitude Ledong, China, a case of double‐layer structures of E‐region FAIs, which occurred at ∼90 and 110 km altitudes respectively, was observed. The double layers of FAIs spanned a zonal coverage ∼250 km, showing as synchronized spatially separated quasi‐periodic patches or wavelike structures in two layers. Especially, the wavelike structures synchronized in the two layers could provide solid evidence for the crucial role of gravity waves in modulating E‐region irregularities. In combination with the collocated ionosonde Es and neutral wind observations, the physical mechanisms responsible for the generation of the double‐layer E‐region FAIs were discussed. Key Points: Double layer structures of E‐region field‐aligned irregularity (FAI) were observed at low latitude by an all‐sky radarBoth layers of FAIs were quasi‐periodic type and synchronously manifested as spatially separated patches or wavelike structuresThe double‐layer FAIs were likely generated by wind shear driven gradient drift instability and modulated by gravity waves [ABSTRACT FROM AUTHOR]

Micro‐Doppler effect is a vital feature of a target that reflects its oscillatory motions apart from bulk motion and provides an important evidence for target recognition with radars. However, establishing the micro‐Doppler database poses a great challenge, since plenty of experiments are required to get the micro‐Doppler signatures of different targets for the purpose of analyses and interpretations with radars, which are dramatically limited by high cost and time‐consuming. Aiming to overcome these limits, a low‐cost and powerful simulation platform of the micro‐Doppler effects is proposed based on time‐domain digital coding metasurface (TDCM). Owing to the outstanding capabilities of TDCM in generating and manipulating nonlinear harmonics during wave‐matter interactions, it enables to supply rich and high‐precision electromagnetic signals with multiple micro‐Doppler frequencies to describe the micro‐motions of different objects, which are especially favored for the training of artificial intelligence algorithms in automatic target recognition and benefit a host of applications like imaging and biosensing. [ABSTRACT FROM AUTHOR]

Radar coincidence imaging (RCI) is a high‐resolution radar imaging mode which constructs a temporal spatial stochastic radiation field (TSSRF) and uses the correlation between reference signal and echoes for imaging. The correlation between the reference matrix and the echoes is the main factor affecting the imaging performance. In fact, radar target characteristics cause fluctuations in the radar cross section and variations in the scattering intensity of each resolution element. The latter degrades the correlation between the reference matrix and the echoes, seriously affecting the image reconstruction. This paper designs the waveform based on image entropy under fixed transmitting and receiving arrays. The targets with fluctuating scattering intensity at each resolution element are statistically modelled. Simulation results show this approach can improve the imaging performance and reduce the energy dissipation degree of the target grid. [ABSTRACT FROM AUTHOR]

The morphology and motion of auroras have been widely studied due to their indications on magnetospheric processes. Here, we report a new kind of "auroral curls," which have wavelengths in the mesoscale (∼100 km) and propagate azimuthally. Utilizing data from the Chinese Antarctic Zhongshan Station (the all‐sky imager and the high‐frequency radar), the Active Magnetosphere and Planetary Electrodynamics Response Experiment and the Defense Meteorological Satellite Program, we analyze an event occurred on 23 April 2019. We find these curls are fine structures in the poleward boundary of multiple arcs. Corresponding field‐aligned currents manifest as a series of longitudinally arranged pairs, while ionospheric flow velocities nearby oscillate with periods in the Pc 5 band. Observational evidence suggests these curls are connected with ultra‐low frequency (ULF) waves, which opens the possibility of using auroras to globally image ULF waves. Plain Language Summary: Auroras caused by precipitation of magnetospheric particles contain information about physical processes happened in the magnetosphere. In this letter, we report a new kind of auroral dynamic forms observed in Antarctica. These structures present both spatial and temporal periodic characteristics, which have similar scales with those of magnetospheric ultra‐low frequency (ULF) waves. We propose these auroral forms are connected with ULF waves, which provides a potential method to globally image ULF waves by analyzing properties of these auroras. Key Points: Azimuthally propagating "auroral curls" with mesoscale wavelengths were observed in AntarcticaThese curls are fine structures in the poleward boundary of multiple arcs formed by longitudinal‐arranged field‐aligned current pairsIonospheric flow velocities nearby oscillate with periods in the Pc 5 band, indicating connections with ultra‐low frequency waves [ABSTRACT FROM AUTHOR]

The dynamical and microphysical aspects of two different precipitating systems have been investigated using the ARIES Stratosphere‐Troposphere Radar (ASTRad) facility and further substantiated by Weather Research Forecasting (WRF) model over Manora Peak. The first event (Case‐I) is associated with the southwest Indian summer monsoon that occurred on 4 August 2020, with a vertical extension of 10–12 km and leads to liquid phase precipitation. The second event (Case‐II) linked to the winter western disturbance occurred on 5 February 2021. This precipitating system was developed with a vertical extension of 6–7 km, resulting in both liquid and solid phase precipitation. Such distinct vertical extension of the systems is found to be associated with the thermodynamical conditions and prevailed large‐scale circulations. By analyzing the vertical structure of these systems using three Doppler moments estimated from the ASTRad (equivalent Reflectivity dBZe, Doppler velocity, and Spectral width), maximum dBZe (∼60 dB) is observed in Case‐II, while higher spectral width (>2 m s−1) is associated to Case‐I. The microphysical processes assessed by the WRF model pointed out that Case‐I involved snow accretion on supercooled droplets, leading to graupel and raindrop formation, while in Case–II, solid and liquid precipitation resulted from ice processes, including accretion or autoconversion. These findings highlight the significance of integrating radar and modeling data to understand the dynamical and microphysical evolution of precipitation under the influence of orography in the Himalayan region. Plain Language Summary: This study examined two different weather events in the Himalayan region. The first event occurred during the monsoon season and mostly involved liquid precipitation, while the second event took place in winter and had both solid and liquid precipitation. To understand how these weather patterns formed in the context of their dynamical and microphysical processes, we analyzed the data obtained from ASTRad technology and the WRF model. The radar data played a crucial role in observing and analyzing the vertical structure and temporal evolution of the precipitation systems. The modeling data provided valuable insights of the orographic effects and microphysical processes behind the formation of solid and liquid phase precipitation. Understanding these processes is essential for predicting weather in the Himalayan region, especially the influence of mountains on liquid and solid precipitation. Key Points: Two distinct precipitation systems, one associated with monsoon and the other with western disturbance are investigated over the central HimalayaThese systems show distinct characteristics in terms of synoptic meteorological conditions, cloud processes, and spatio‐temporal scalesIntegrated radar and model data emphasize the importance of understanding the precipitation evolution influenced by orography over the region [ABSTRACT FROM AUTHOR]

The joint transmit and receive beamforming algorithm has been envisioned to optimise the target's signal‐to‐interference‐plus‐noise ratio by co‐designing the transmit and receive beamforming vectors simultaneously. However, the traditional design concepts for this algorithm only consider the monostatic radar system, which may not work as well for the multistatic radar system. The authors present a novel transmit and receive beamforming algorithm for the multistatic radar system that includes a common transmitter and multiple receivers. The transmit beamforming vector can directly influence the output signal‐to‐interference‐plus‐noise ratio of each radar subsystem. To ensure the balanced performance of the subsystems, the authors propose a weighted sum of the signal‐to‐interference‐plus‐noise ratio optimisation problem to co‐design the transmit and receive beamforming. The proposed problem is non‐convex, and the authors construct an iterative algorithm to solve it using semi‐definite relaxation and slack‐variable replacement techniques. By using pre‐determined weights, the output performance of each radar subsystem can be effectively regulated. The simulation results confirm that the proposed algorithm can ensure better output signal‐to‐interference‐plus‐noise ratio for the entire multistatic radar system than the conventional joint transmit and receive beamforming algorithm. [ABSTRACT FROM AUTHOR]

In the current complex situations of electronic intelligence (ELINT), the authors present a radar emitter structure (RES) identification method based on deep learning at a new level to address the issue of incomplete cognitive information. Firstly, due to the fact that existing simulation data cannot fully reflect the structure features of the entire radar emitter, the structure feature‐level RES model is built using direct digital synthesiser (DDS) technology and radio frequency (RF) simulation platform. Afterwards, considering that the structure features are reflected in the frequency domain, a stacked frequency sparse auto encoder (sFSAE) network is constructed by adding a constraint term in frequency domain to the loss function of sparse auto encoder (SAE). Using deep learning to extract structure features with constraints in different domains is instructive for feature extraction techniques under variable operating parameters. Finally, the extracted structure features are input into the Softmax classifier to perform the identification from the radar signal to the RES. The experimental results show that the proposed method has high generalisation ability and robustness under different modulation types, different operating parameters and different signal to noise ratio (SNR). It also has a high identification rate even for untrained modulated signals. [ABSTRACT FROM AUTHOR]

The application and uses of drones in all areas are continuously rising, especially in civilian use cases. This increasing threat requires reliable drone surveillance in urban environments. Radar is the obvious candidate with its ability to detect small objects at range, in all weather conditions. The use of an L‐band networked radar for urban sensing of S‐UAS targets is explored. Small echoes from S‐UAS places a premium on synchronisation, which is the fundamental key for high performance networked radar. The effect of timing errors on the operation of the network radar is investigated theoretically and experimentally, and the processing tools for synchronising data based on the direct signal returns of the transmitter are developed. Also, drone detection using bistatic L‐band staring radar is achieved both in simulation and then in real field trials where the SNR and detection performance are computed and analysed. The updated direct signal synchronisation method for bistatic staring radar is shown to provide comparable SNR and positional accuracy for S‐UAS targets as the monostatic staring radar. [ABSTRACT FROM AUTHOR]

The authors present the design, fabrication and testing of a W‐band monopulse antenna. This antenna consists of three main building blocks: an array with 48 × 48 slots, a tapered corporate‐feed network and a comparator. The comparator has four input ports to generate sum and difference patterns in orthogonal cardinal planes for monopulse operation. A novel octuple excitation is adopted for the corporate network to achieve a side lobe level lower than −20 dB for the sum patterns and simultaneously reduce drastically the manufacturing complexity. Diffusion bonding technology is used for fabrication. Twenty‐four etched copper sheets with a thickness of 0.2 mm are stacked to realise the prototype. The total size of the antenna is 40.76×40.76×1.51λ03 $40.76\times 40.76\times 1.51{\lambda }_{0}^{3}$, with λ0 being the wavelength at the centre frequency (94 GHz). The antenna presents an isolation better than 12 dB among the input ports in the 84–100 GHz band. The realised measured gain is 40.7 dBi at 94 GHz. The measured 3‐dB gain bandwidth is 17.4%. The difference patterns at 94.0 GHz present null depths of −24.0, −18.0, −17.5, and −17.4 dB, in the E‐, H‐, 45°‐, and 135°‐planes, respectively. [ABSTRACT FROM AUTHOR]

An exceptionally strong westward propagating quasi‐6‐day wave (Q6DW) with zonal wavenumber 1 in connection with the rare 2019 Southern Hemispheric Sudden Stratospheric Warming (SSW) is observed by two meteor radars at 30°S and is found to modulate and interact with the diurnal tide and gravity waves (GWs). The diurnal tide is amplified every 6 days and a prominent 21 hr child wave attributed to Q6DW‐diurnal tide nonlinear interaction occurs. Q6DW modulation on GWs is confirmed as the 4–5 day periodicity in GW variances. Simultaneously, the Q6DW appears to shift its period toward the periodicity of the modulated GW variances. Enhancement is also observed in the first results of meteor radar observed Q6DW Eliassen‐Palm flux, which may facilitate the global perturbation and persistence of this Q6DW. We conclude that the observed SSW triggered Q6DW‐tide and Q6DW‐GW interactions play an important role in coupling the lower atmospheric forcings to ionospheric variabilities. Plain Language Summary: Our work provides observational evidence for the 6‐day planetary wave‐tide and 6‐day planetary wave‐gravity wave interactions at the Earth's mesosphere and lower thermosphere. The results strongly support the theory that wave‐wave interactions are the primary mechanism coupling planetary waves to ionospheric variability and provide an additional mechanism as the 6‐day wave modulation on the gravity waves. We utilize measurements from two meteor radars to diagnose planetary wave characteristics and identify wave‐wave interactions, and compute the first‐time meteor radar observed Eliassen‐Palm flux. Enhancement is observed in the Eliassen‐Palm flux of 6‐day wave following the SSW maximum phase, which demonstrates that energy of the 6‐day wave is enhanced and therefore, facilitates the global perturbation and persistence of the 6‐day wave for an extended time period. While meteor radar observations are widely used to investigate planetary waves and tides, high meteor detection rate is required for further studying temperature perturbations and small scale waves (e.g., gravity waves). Thus, this work also highlights the capability of a modern multi‐static meteor radar system, Chilean Observation Network De meteOr Radars, in resolving oscillations of small spatial scales over a broad range of periods, and for calculating Eliassen‐Palm flux of planetary waves. Key Points: A dominating W1 Q6DW is observed at 30°S and its Eliassen‐Palm flux is enhanced during the 2019 SH SSWQ6DW amplifies the diurnal tide every 6 days and a strong 21 hr child wave is observedQ6DW modulates the gravity wave variances and its frequency appears to shift accordingly [ABSTRACT FROM AUTHOR]

Surface air pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Few measurements from buoys and ships are available over ocean. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: first the electromagnetic fields are absorbed mainly by oxygen and water vapor, and second that oxygen is well mixed. In this work we discuss a space‐borne concept, which aims at providing, over the ocean, consistent, and regular observations for determining surface air pressure from space by a design of a multi‐tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of‐the‐art microphysical retrievals applied to the A‐Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapor, cloud liquid water and rain water and the potential of a correction via the three‐tone method is discussed. Errors introduced by surface measurement noise and temperature profile uncertainties are discussed as well. Results show that accuracy between 2 and 5 hPa is at reach. Plain Language Summary: Pressure is an important atmospheric variable. A radar‐based remote sensing techinque to measure surface pressure is proposed. The methodology is based on the darkening of the surface return when moving closer to the center of the 60 GHz oxygen absorption line. Results demonstrate that measurements of surface pressure with errors of few hPa are feasible. Key Points: Differential absorption radars with multiple tones within the oxygen band to enable surface pressure measurements with few hPa accuracyCloud, rain and water vapor produce biases in the surface pressure that must be correctedErrors can be minimized by adopting tunable frequencies and frequency diversity [ABSTRACT FROM AUTHOR]

Convection‐generated gravity waves (CGWs) transport momentum and energy, and this momentum is a dominant driver of global features of Earth's atmosphere's general circulation (e.g., the quasi‐biennial oscillation, the pole‐to‐pole mesospheric circulation). As CGWs are not generally resolved by global weather and climate models, their effects on the circulation need to be parameterized. However, quality observations of GWs are spatiotemporally sparse, limiting understanding and preventing constraints on parameterizations. Convection‐permitting or ‐resolving simulations do generate CGWs, but validation is not possible as these simulations cannot reproduce the CGW‐forcing convection at correct times, locations, and intensities. Here, realistic convective diabatic heating, learned from full‐physics convection‐permitting Weather Research and Forecasting simulations, is predicted from weather radar observations using neural networks and a previously developed look‐up table. These heating rates are then used to force an idealized GW‐resolving dynamical model. Simulated CGWs forced in this way closely resembled those observed by the Atmospheric InfraRed Sounder in the upper stratosphere. CGW drag in these validated simulations extends 100s of kilometers away from the convective sources, highlighting errors in current gravity wave drag parameterizations due to the use of the ubiquitous single‐column approximation. Such validatable simulations have significant potential to be used to further basic understanding of CGWs, improve their parameterizations physically, and provide more restrictive constraints on tuning with confidence. Plain Language Summary: Thunderstorms generate waves in the atmosphere that can generate turbulence at commercial aircraft cruising altitudes and further aloft. At these higher altitudes, they eventually break, not only generating turbulence, but also exerting forces that affect the large‐scale flows in the middle atmosphere. While these waves have been known to be important since at least the 1980s, they are difficult to observe. They can be simulated, but weather models do not simulate thunderstorms in the correct locations at the right times, meaning the simulated waves cannot be directly compared against observations. Here, weather radar observations are used as input to a look‐up table and a neural network to force realistic thunderstorm motions and waves within a simplified weather model. This method was able to reproduce a satellite‐observed case with notable skill. In one of the first simulations of thunderstorm‐generated waves comparable to satellite observations, these waves travel 100s of kilometers away from the thunderstorms, conflicting with assumptions made in weather and climate models. Key Points: If realistic convective diabatic heating is supplied at the correct places and times, GW‐resolving models can reasonably reproduce Convection‐generated gravity waves (CGWs)While location and type of convective storm do influence latent heating, these difference are of second order importance for CGW forcingDrag due to convection‐generated GWs from a compact source region is spread over O(1,000) km due to lateral propagation [ABSTRACT FROM AUTHOR]

Detection of space objects is a key component of space situational awareness, which could help prevent and minimise space collisions. While there have been lots of radar systems designed to detect space objects, few of them have dealt with long baseline distributed bistatic pairs. The authors focus on the feasibility of long baseline bistatic radars, which can be extended for the multistatic case; and the performance of the multistatic system for a target at different altitudes assuming one transmitter over three different scenarios: a cluster of receivers, receivers spread throughout the world and the combination of the two previous cases. To analyse the performance the multiple‐input‐multiple‐output (MIMO) ambiguity function (AF) will be employed. The results of the MIMO AF show how the fusion of different bistatic pairs improves the detection capabilities. Moreover, when the different radar measurements are coherently summed in the MIMO AF, the uncertainty on the location of the target is reduced. [ABSTRACT FROM AUTHOR]

The increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio‐telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range‐Doppler maps, then used as inputs for object detection exploiting You‐Only‐Look‐Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications. [ABSTRACT FROM AUTHOR]

Equivalent‐time sampling can improve measurement or sensing systems because it enables a broader frequency band and higher delay resolution for periodic signals with lower sampling rates than a Nyquist receiver. Meanwhile, a digital down‐conversion (DDC) technique can be implemented using a straightforward radio frequency (RF) circuit. It avoids timing skew and in‐phase/quadrature gain imbalance instead of requiring a high‐speed analog‐to‐digital converter to sample an intermediate frequency (IF) signal. Therefore, when equivalent‐time sampling and DDC techniques are combined, a significant synergy can be achieved. This study provides a parameter design methodology for optimal equivalent‐time sampling using DDC. [ABSTRACT FROM AUTHOR]

Conflict game theory is employed to analyze the countermeasure relationship between radar systems and jammers. To address this issue, a novel utility function and a Conflict Power Allocation Game (CPAG) algorithm are introduced for determining the Nash Equilibrium (NE) solution of the game. The utility function enables the calculation of optimal power response functions for both the radar system and the jammer, with the NE solution of the CPAG algorithm obtained through numerical computation. Simulation results demonstrate the convergence of the proposed CPAG algorithm to the NE solution, validating its efficacy. Finally, an analysis of the relationship between the utility function and pricing factors is conducted based on the proposed CPAG algorithm and simulation outcomes.This article introduces a novel utility function and a Conflict Power Allocation Game (CPAG) algorithm to analyze the countermeasure game dynamics between radar and jammer systems. By deriving optimal response functions using the utility function, the Nash Equilibrium (NE) solution of the game is calculated, demonstrating convergence of the CPAG algorithm. Simulation results validate the efficacy of the proposed approach, highlighting the relationship between the utility function and pricing factors. [ABSTRACT FROM AUTHOR]

This article is a guest editorial that discusses advancements and future trends in noise radar technology. Noise radar technology uses random and non-periodic radar signals to eliminate ambiguities present in other radars. The article explores the practical aspects of noise radar technology, such as evaluating the degree of randomness in transmission and the computational load of signal processing. It also covers topics like electronic warfare, anti-intercept features, and the potential applications of artificial intelligence in noise radar technology. The article includes specific papers on implementation aspects, waveform selection, anti-intercept features, and artificial intelligence applications in noise radar technology. The authors hope that this special issue provides valuable insights into the advancements and future trends of noise radar technology. [Extracted from the article]

Turbulence in clouds is known to enhance particle collision rates, as widely demonstrated for warm rain formation. A similar impact on ice growth processes is expected but a solid observational basis is missing. A statistical analysis of a 15‐month data set of cloud radar observations allows for the first time to quantify the impact of turbulence on ice aggregation and riming in Arctic low‐level mixed‐phase clouds. Increasing eddy dissipation rate (EDR), from below 10−4 to above 10−3 m2 s−3, yields larger ice aggregates, and higher particle concentration, likely caused by increasing fragmentation. In conditions more favorable to riming, higher EDR is associated with dramatically higher particle fall velocities (by up to 125%), under similar liquid water paths, indicative of markedly higher degrees of riming. Our findings thus reveal the key role of turbulence for cold precipitation formation, and highlight the need for an improved understanding of turbulence‐hydrometeor interactions in cold clouds. Plain Language Summary: Liquid and frozen precipitation mainly forms by collision and subsequent aggregation of small particles. Collisions between cloud particles, such as droplets and ice crystals, are thought to be increased by turbulence. While this effect has been intensively studied for liquid‐only clouds, the impact of turbulence on ice‐ice collisional growth (aggregation) and ice‐liquid collisional growth (riming) is expected but has so far been poorly quantified. We study the effect of turbulence on aggregation and riming based on a long‐term remote‐sensing data set of low‐level clouds containing both ice and liquid particles, recorded at the Arctic site of Ny‐Ålesund, Svalbard. Cloud radar observations are used to retrieve the dissipation rate of turbulent kinetic energy (i.e., the eddy dissipation rate; EDR), which is the relevant quantity driving increases in collision rates, and to characterize ice particle properties. We find evidence that higher EDR regimes enhance the aggregation of particles, and are associated with signatures of increased ice particle concentration, possibly caused by the production of particle fragments upon collision. In temperature regimes more favorable to riming, turbulence dramatically enhances the particles' fall velocity, denoting higher degrees of riming. Our findings thus highlight a key role of turbulence for the formation of precipitable ice. Key Points: Relation between turbulence and ice growth investigated based on long‐term remote sensing data set of Arctic low‐level mixed‐phase cloudsHigher eddy dissipation rate (EDR) correlates with larger ice aggregates, and possibly higher degrees of fragmentationHigh EDR is an essential component needed for the formation of rimed particles [ABSTRACT FROM AUTHOR]

Due to the development of digital radio frequency memory (DRFM), active jamming against the main lobe of the radar has become mainstream in electronic warfare. The jamming infiltrates the radar receiver via the main lobe, covering up the target echo information. This greatly affects the detection, tracking, and localization of targets by radar. In this study, we consider jamming suppression based on the independence of RF features. First, two stacked sparse auto-encoders (SSAEs) are built to extract the RF characteristics and signal features carried out by the actual radar signal for subsequent jamming suppression. This method can effectively separate RF features from signal features, making the extracted RF features more efficient and accurate. Then, an SSAE-based jamming suppression auto-encoder (JSAE) is proposed; the mixed signal, including the radar signal, jamming signal, and noise, is input to JSAE for dimensionality reduction. Therefore, the radar signal and RF features, extracted by the two SSAEs in the previous step, are used to constrain the features of the reduced mixed signal. Moreover, we integrate the feature level and signal level to jointly achieve jamming suppression. The original radar signal is used to assist the radar signal reconstructed by the decoder. By first filtering out interference-related features and then reconstructing the signal, we can achieve better jamming suppression performance. Finally, the effectiveness of the proposed method is verified by simulating the actual collected data. [ABSTRACT FROM AUTHOR]

The Integrated Side Lobe Ratio (ISLR) problem the authors consider here consists in finding optimal sequences of phase shifts in order to minimise the mean squared cross‐correlation side lobes of a transmitted radar signal and a mismatched replica. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP‐hard. In their work, the authors aim to take advantage of the scalability of quantum computing to find new optima, by solving the ISLR problem on a quantum annealer. This quantum device is designed to solve quadratic optimisation problems with binary variables (QUBO). After proposing suitable formulation for different instances of the ISLR, the authors discuss the performances and the scalability of their approach on the D‐Wave quantum computer. More broadly, their work enlightens the limits and potential of the adiabatic quantum computation for the solving of large instances of combinatorial optimisation problems. [ABSTRACT FROM AUTHOR]