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Multiple‐input multiple‐output (MIMO) radar waveform design in a spectrally crowded environment is a challenging problem. In this study, the issue of MIMO radar waveform design in coexistence with communication systems is investigated by maximising the output signal‐to‐interference‐plus‐noise ratio (SINR) of the MIMO radar under transmit energy, similarity and spectral compatibility constraints. Different from the existing alternating direction method of multipliers (ADMM) proposed in a single‐antenna scenario, a novel ADMM algorithm for spectrally compatible MIMO radar waveform design is proposed to efficiently tackle the non‐convex optimisation problem in both single‐antenna and multi‐antenna scenarios. Due to the decomposability of ADMM, only a few quadratic programming subproblems need to be solved during each iteration in the authors' algorithm, which reduces the computational complexity. In numerical simulations, the proposed ADMM algorithm is compared with the semidefinite relaxation algorithm and the existing ADMM algorithm in a single‐antenna scenario and a multi‐antenna scenario, respectively. It shows that the proposed ADMM algorithm can achieve almost the same performance as the two existing algorithms; however, it is computationally more efficient. [ABSTRACT FROM AUTHOR]

Due to the technical barriers between radars and jammers and the poor performance of the traditional detection‐jamming shared signal in integrated radar‐electronic warfare systems, a new detection‐jamming shared signal waveform based on the virtual force field algorithm (VFFA) is proposed in this article. First, a multiobjective and multidimensional characteristic parameter optimization model, based on a virtual force field, is established, and then the design principle of the shared signal is presented in detail. The simulation results show that the detection‐jamming shared signal based on the VFFA presents the deceptive jamming of multiple false targets in a noncollaborative target's radar screen. Further, there is better detection performance with the advantages of multiple pulse repetition frequency (PRF) and pulse accumulation numbers, which are highly sensitive to the multijagged PRF signals emitted by the noncollaborative target. According to the VFFA described in this article, the optimum detection‐jamming shared signal waveform can be output in real time for specific free space targets, to improve the efficiency of integrated radar and electronic warfare systems. [ABSTRACT FROM AUTHOR]

Digital programmable metasurfaces (DPMs) can dynamically modulate electromagnetic waves in space, time, and frequency domains with powerful capabilities. In contrast, joint radar and communication (JRC) is an important avenue to use the electromagnetic waves universally and intelligently. Hence, combining DPM and JRC may produce a promising way to achieve universal and intelligent electromagnetic terminals. Herein, a mechanism of extremely flexible JRC based on DPM is proposed, in which DPM can improve the flexibility of JRC in processing multidimensional information, and JRC endows DPM with the capabilities of perception, expression, and cognition to promote its intelligent level. Three kinds of functions are explored to interpret the mechanism. First, encryption and decryption of wireless secure communications are realized through DPM; second, range profile imaging is accomplished by using DPM‐based biphase‐coded signals; and third, wireless communication and radar detection are simultaneously presented using DPM. The combination of DPM and JRC greatly decreases the complexity of signal processing and simplifies the architecture of the JRC system, and promotes the intelligent level of DPM. [ABSTRACT FROM AUTHOR]

Dual‐polarization radars can provide rich three‐dimensional (3D) information on cloud precipitation structure. To utilize the existing polarimetric radar network in operational data assimilation systems, a new polarimetric radar‐based cloud analysis method is introduced. New features include the employment of fuzzy‐logic hydrometeor classification, improved estimation of liquid and ice regions, and newly‐added number concentration estimation. The new scheme is evaluated with a typical squall line case. Results show that the 3D cloud precipitation structure and short‐term precipitation forecast is consistently improved. With extra information from the polarimetric observations, the new scheme is able to produce reasonable polarimetric signatures for analysis. More supercooled water results in more latent heat release which enhances the updraft, leading to stronger convection in the subsequent forecast. This study emphasizes the importance of correct initial liquid and ice particle condition for the prediction of deep convection. Plain Language Summary: Severe convective storms have always been one of the most the disastrous types of weather. However, due to their relatively small scale and rapid evolution, such storms are difficult to predict. Polarimetric radar networks have been established for nearly a decade and have been widely used in the monitoring and short‐term forecast of severe weather. Yet, this extra polarimetric information has not been used in the operational data assimilation system, which is key to a successful forecast. A new dual‐polarization (dual‐pol) radar‐based cloud analysis aiming to improve short‐term forecasts is therefore proposed. Results show that the new scheme is able to capture the 3D in‐cloud signature of deep convection. Consistent improvements are found for the prediction of 3D structure of deep convection and as well as for the quantitative precipitation forecast. Key Points: A modified cloud analysis model is proposed for polarimetric radar data assimilationA fuzzy‐logic method combined with ZDP is used to classify hydrometeor particles, followed by a partially double‐moment retrieval schemeExperiments with polarimetric‐data‐based cloud analysis show consistent improvement on analysis and forecast [ABSTRACT FROM AUTHOR]

Exploration of Venus in the 1970–1990s revealed that the geology of Venus, the most Earth‐like of the terrestrial planets, was decidedly un‐Earth‐like, with no plate tectonics, and no record of the first 80% of its history. A major outstanding question is whether Venus is still volcanically active today. We find that regions of Ganis Chasma have low radar emissivity values, due to low volumes of high dielectric minerals formed by surface‐atmosphere weathering on the timescales of around 10 s Ma. This confirms the presence of geologically recent volcanism in association with this major tectonic rift zone. The spatial correspondence of this emissivity signature with transient thermal anomalies suggests that Venus has been volcanically active at this site for at least the last few decades, a prediction that can be tested with space missions to Venus in the coming decade. Key Points: Radar anomalies in Ganis Chasma correspond to transient bright spots seen in infrared dataLow radar emissivity values suggest recent volcanic and tectonic activity on VenusLava flows in Ganis Chasma may have erupted over the last 30 years [ABSTRACT FROM AUTHOR]

Passive radar systems use illuminators of opportunity to illuminate targets instead of dedicated radar transmitters. The signals of opportunity have lower bandwidth than dedicated active radar systems, leading to poor downrange resolution. Multiple signals of opportunity can be coherently combined to increase the overall bandwidth of the system, and therefore create finer resolution images. These signals are usually separated in the frequency domain (non‐contiguous), which causes large unwanted grating lobe artefacts in the image when using back‐projection or Fourier transform based imaging. Additionally, these signals may originate from transmitters not located at the same position. A calibration method is developed to align the downrange responses and coherently combine the two signals. A compressive sensing‐based algorithm is used to combine the calibrated non‐contiguous frequency data, and is shown to mitigate the grating lobe artefacts that occur when using a back‐projection algorithm. This approach is validated with simulated and experimental data from a passive radar system tracking a commercial airline using two digital television (DTV) broadcast transmitters. [ABSTRACT FROM AUTHOR]

The direction and position of the radar signals are essential information in the radar countermeasure, and with the development of the reconnaissance system, the radar is facing more severe threats. However, the high‐gain beam and even‐distribute phase of the phased‐array radar restrict the further development of radar protection measures. Therefore, considering the angle‐range‐time‐coupled beam and phase of the frequency diverse array (FDA), we proposed a deceptive signal generating method based on the FDA. Firstly, the amplitude‐ and phase‐based reconnaissance countermeasures were established, respectively. Moreover, the regulation ability of the frequency increment sequence was studied. Therefore, by finding the appreciated frequency increment sequence that can regulate the FDA signals, the proposed FDA can generate deceptive signals that counter both the amplitude‐ and phase‐based reconnaissance system. The instantaneous signal‐to‐noise ratio and Cramér–Rao lower bound were considered to verify the method. Numerical examples and simulations showed that the proposed countermeasure could generate deceptive signals with good error tolerance, whether in a noisy or non‐noise environment. [ABSTRACT FROM AUTHOR]

Rapid impact assessment of cyclones on coastal ecosystems is critical for timely rescue and rehabilitation operations in highly human‐dominated landscapes. Such assessments should also include damage assessments of vegetation for restoration planning in impacted natural landscapes. Our objective is to develop a remote sensing‐based approach combining satellite data derived from optical (Sentinel‐2), radar (Sentinel‐1), and LiDAR (Global Ecosystem Dynamics Investigation) platforms for rapid assessment of post‐cyclone inundation in non‐forested areas and vegetation damage in a primarily forested ecosystem. We apply this multi‐scalar approach for assessing damages caused by the cyclone Amphan that hit coastal India and Bangladesh in May 2020, severely flooding several districts in the two countries, and causing destruction to the Sundarban mangrove forests. Our analysis shows that at least 6821 sq. km. land across the 39 study districts was inundated even after 10 days after the cyclone. We further calculated the change in forest greenness as the difference in normalized difference vegetation index (NDVI) pre‐ and post‐cyclone. Our findings indicate a <0.2 unit decline in NDVI in 3.45 sq. km. of the forest. Rapid assessment of post‐cyclone damage in mangroves is challenging due to limited navigability of waterways, but critical for planning of mitigation and recovery measures. We demonstrate the utility of Otsu method, an automated statistical approach of the Google Earth Engine platform to identify inundated areas within days after a cyclone. Our radar‐based inundation analysis advances current practices because it requires minimal user inputs, and is effective in the presence of high cloud cover. Such rapid assessment, when complemented with detailed information on species and vegetation composition, can inform appropriate restoration efforts in severely impacted regions and help decision makers efficiently manage resources for recovery and aid relief. We provide the datasets from this study on an open platform to aid in future research and planning endeavors. [ABSTRACT FROM AUTHOR]

This paper provides analyses to quantify the maturity of regolith within 24 m depth using a novel polarimetric attributes analysis of lunar penetrating radar (LPR) data. The results demonstrate that LPR signals were mainly from subsurface rock fragments of different sizes and were rarely from strata interfaces. The averages and ranges of maturities for the near‐surface (0–2 m), the fine‐grained regolith (2–12 m), and the coarse‐grained ejecta (12–24 m) are 68.98% (0%–99.82%), 82.61% (41.52%–96.56%), and 72.33% (5.91%–96.66%), respectively. Local regions with anomalously low maturities on the lunar surface may be due to the dense materials formed by local impacts or the rock fragments from distal impacts; a newly discovered sandwich structure with low maturity at a depth between 12 and 18 m is formed by the heterogeneous weathering in a paleo‐crater. Based on these new insights, we infer a heterogeneous weathering process of the regolith. Plain Language Summary: The entire lunar surface is considered to be covered by a layer of regolith. The study of regolith weathering degree almost focused on the near‐surface because the traditional techniques cannot detect the regolith at large depths. The Chang'E‐4 lunar penetrating radar (LPR) can detect the regolith structure within dozens of meters depth. However, previous publication of LPR results only use the macro layering model to interpret the regolith structure. This study innovatively extracts new properties from the LPR data acquired while the rover was turning and estimated the quantitative maturity of regolith within ∼24 m depth at lunar farside. We found that the LPR can mainly detect the subsurface rock fragments that survived weathering and rarely the interfaces of strata because the materials of different strata are mixed at interfaces and make it gradational. Our results also reveal the spatial difference of weathering in the regolith. We investigate the formations and material compositions of several interesting regions with unusual weathering degrees. Based on these new insights, we establish a model to illustrate the weathering process of the regolith at the CE‐4 landing site. Key Points: We quantify the maturity of the lunar regolith within 24 m depth by analyzing the lunar penetrating radar (LPR) data acquired during the rover turningsLPR signals were mainly from subsurface rock fragments of different sizes and were rarely from strata interfacesInferring the heterogeneous weathering process by comprehensively investigating the regions with anomalous maturities in the regolith [ABSTRACT FROM AUTHOR]

Retroreflectors are ubiquitously used in a multitude of applications, such as cloaking, wireless communication, radar, and antenna, owing to their ability to augment the reflected electromagnetic (EM) waves in the incident direction. However, Current metasurface retroreflector designs have yet to mature into a practical method due to the limitations of low efficiency and narrow band, which actually originate from the difficulty in simultaneously engineering phase profiles of certain metasurface at distinct wavelengths. Here, a broadband spin‐locked retroreflector with high efficiency that relies only on a simple metasurface layer is demonstrated. The metasurface is designed with low‐loss dielectric resonators, introducing both the propagation and geometric phases to enable dispersive phase compensation. The results indicate that the proposed metasurface can achieve retroreflection over a broadband spectrum while keeping the spin state identical. Furthermore, a broadband spin‐locked cloak is presented for validation. The work builds up a major advance for practice‐oriented retroreflector and even envision this approach may open new vistas in the very cutting‐edge research of 6G wireless communication network. [ABSTRACT FROM AUTHOR]

Compared with traditional approaches that used vision sensors which can provide a high‐resolution representation of targets, millimetre‐wave radar is robust to scene lighting and weather conditions, and has more applications. Current methods of human skeletal pose estimation can reconstruct targets, but they lose the spatial information or don't take the density of point cloud into consideration. We propose a skeletal pose estimation method that combines point convolution to extract features from the point cloud. By extracting the local information and density of each point in the point cloud of the target, the spatial location and structure information of the target can be obtained, and the accuracy of the pose estimation is increased. The extraction of point cloud features is based on point‐by‐point convolution, that is, different weights are applied to different features of each point, which also increases the nonlinear expression ability of the model. Experiments show that the proposed approach is effective. We offer more distinct skeletal joints and a lower mean absolute error, average localisation errors of 6.1 cm in X, 3.5 cm in Y and 3.3 cm in Z, respectively. [ABSTRACT FROM AUTHOR]

Air Defence System (ADS) effectiveness relies not only on weapon and sensor performances but also on effective usage of those resources. In an ADS Network, several ADS are deployed in an overlapping coverage in order to increase the protection of defended assets (DA). Limited tracking capacity of ADS fire control radars leads to a limited engagement capability of the whole ADS network. In this paper, a radar resource optimisation algorithm is proposed for networked ADS fire control radars in order to apply an adaptive tracking strategy for threatening targets under the constraint of tracking capacity limitation for each fire control radar. Threat posed by the targets is calculated using a Fuzzy Logic System (FLS) that takes into account the variables related to threat as well as DAs. An optimisation strategy is utilised in order to select the minimum number of tracking radars that will minimise the cost function formed by tracking quality metrics related to threat priorities and Bayesian Cramér–Rao lower bound (BCRLB). Proposed algorithm dynamically selects target tracking quality and minimises tracking resources in an ADS by utilising surrogate optimisation suitable for real‐time implementation. Simulation results are given in order to show the improvement in defence effectiveness of networked ADS. [ABSTRACT FROM AUTHOR]

In the past few decades, the target location methods in transmit/receive‐receive (T/R‐R) synergetic High Frequency Surface Wave Radar (HFSWR) have received extensive attention from the signal processing community. However, there are few studies on the method of parameter pair‐matching for multi‐target scene. In this paper, a computationally efficient multi‐target pair‐matching method based on angle information only in T/R‐R synergetic HFSWR is developed. The proposed method is realised by dealing with three vectors composed of the first column, the first row and diagonal entries of the correlation matrix, instead of performing the singular value decomposition (SVD) of the correlation matrix. Simultaneously, the proposed method is a computationally efficient subspace algorithm, which only uses the angle information to complete the purpose of multi‐target pair‐matching. The simulation results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Frequency diverse array‐multiple input multiple output (FDA‐MIMO) radar not only has the advantage of waveform diversity but also can realise azimuth‐range correlation map, so it can realise high precision azimuth‐range joint estimation and has the ability to distinguish different range unit targets in the same azimuth. However, the existing research mainly adopts uniform linear array (ULA) model and mainly focusses on incoherent targets. At present, the elevation‐range joint estimation algorithm of metre wave FDA‐MIMO radar based on ULA has the problems of sharp decline in angle‐range measurement accuracy of ultra‐low altitude targets and large fluctuation of elevation‐range measurement accuracy with changing elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, this paper innovatively combines sparse array with FDA‐MIMO radar, establishes the low elevation‐range joint estimation model of metre wave FDA‐MIMO radar based on sparse array, and puts forward the low elevation‐range joint estimation method suitable for this model, and it fills the gap of low elevation‐range measurement method based on sparse array metre wave FDA‐MIMO radar. Simulation experiments compare two typical sparse arrays with ULA of equal array elements, which verify the superiority of low elevation‐range measurement performance of monostatic metre wave FDA‐MIMO radar based on sparse array and the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Continuous Human Activity Recognition (HAR) in arbitrary directions is investigated in this paper using a network of five spatially distributed pulsed Ultra‐Wideband radars. While activities performed continuously and in unconstrained trajectories provide a more realistic and natural scenario for HAR, the network of radar sensors is proposed to address the issue of unfavourable or occluded perspectives when using only a single sensor. Different techniques to combine the relevant information from the multiple radars in the network are investigated, focussing on signal level fusion directly applied on Range‐Time maps, and the selection of radar nodes based on location and velocity of the target derived from multilateration processing and tracking. Recurrent Neural Networks with and without bidirectionality are used to classify the activities based on the micro‐Doppler (μD) spectrograms obtained for sensor fusion techniques. To assess classification performances, novel evaluation metrics accounting for the continuous nature of the sequence of activities and inherent imbalances in the dataset are proposed and compared with existing metrics. It is shown that the conventional accuracy metric may not capture all the important aspects for continuous HAR, and the proposed metrics can be considered for a more comprehensive evaluation. [ABSTRACT FROM AUTHOR]

In this paper, the waveform design for Rician target detection with multiple‐input‐multiple‐output radar is addressed. The relative entropy between the distributions of the observations under two hypotheses is employed as the design metric. Due to the difficulty of tackling the non‐convex waveform design problem, a novel algorithm based on minorisation‐maximisation is developed. Since a simple quadratic function is established to minorise the objective function, the presented algorithm has lower computational complexity than its counterparts. Moreover, it can be extended to design waveforms under many practical constraints, including the constant‐envelope constraint, the similarity constraint, and the constant‐envelope and similarity constraints. Numerical results are provided to show the effectiveness of the proposed algorithm for detecting Rician targets. Moreover, the designed waveforms can be used to enhance the detection performance of a Swerling 0 target in the presence of angle mismatch. [ABSTRACT FROM AUTHOR]

Software defined radar (SDRadar) systems have become an important area for future radar development and are based on similar concepts to Software defined radio (SDR). Most of the processing like filtering, frequency conversion and signal generation are implemented in software. Currently, radar systems tend to have complex signal processing and operate at wider bandwidth, which means that limits on the available computational power must be considered when designing a SDRadar system. This paper presents a feasible solution to this potential limitation by accelerating the signal processing using a GPU to enable the development of a high speed SDRadar system. The developed system overcomes the limitation on the processing speed by CPU‐only, and has been tested on three different SDR devices. Results show that, with GPU accelerator, the processing rate can achieve up to 80 MHz compared to 20 MHz with the CPU‐only. The high speed processing makes it possible to run in real‐time and process full bandwidth across the WiFi signal acquired by multiple channels. The gains made through porting the processing to the GPU moves the technology towards real‐world application in various scenarios ranging from healthcare to IoT, and other applications that required significant computational processing. [ABSTRACT FROM AUTHOR]

The IET Radar, Sonar & Navigation (RSN) journal has a history of publishing special issues focused on new technologies or conference papers. They plan to continue and expand these special issues under the oversight of Professor Alessio Balleri. The journal has also embraced the open access model, with 27 of their hybrid journal programs transitioning to Gold OA in 2020. They now have 43 open access journals in their portfolio. Open access offers benefits such as wider readership, faster impact, and increased recognition for authors. The RSN journal prides itself on its rapid processing and publication times, with an average time of 29 days from submission to first decision and 25 days from acceptance to e-first publication. The editors express their gratitude to the anonymous reviewers for their contributions to maintaining the journal's quality and speed. The article concludes with a statement that no new data were created or analyzed during the study. [Extracted from the article]

The integrated radar and communications (IRC) waveform can simultaneously estimate the range and velocity of other targets while communicating with other facilities. In high‐speed scenarios, the IRC echo signal based on orthogonal frequency division multiplexing (OFDM) needs to consider the intra‐symbol and inter‐subcarrier Doppler effects. This paper first builds a receiving model of the integrated echo signal by including the intra‐symbol and inter‐subcarrier Doppler effects. On this basis, we propose a two‐step joint range and velocity estimation method, where rough estimation is performed by an improved estimating the signal parameters via the rotational invariance technique (ESPRIT), and a least‐square weighted (LSW) method is applied for fine estimation. The Cramer‐Rao lower bound (CRLB) of the receiving model is also provided, and the computational complexity of the proposed method is analyzed. Simulation results demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Sparse frequency agile linear frequency modulation-orthogonal frequency division multiplexing (LFM-OFDM) radar can improve anti-interference ability as well as reduce the sampling rate of the radar system. However, the frequency agility makes it difficult to realize coherent processing by using the fast Fourier transform. To overcome this problem, a signal processing scheme for the sparse frequency agile LFM-OFDM radar is proposed to obtain the high range and Doppler resolution in this study. The subcarriers are synthesised to an LFM signal in the time domain at first. Then, within the framework of the compressed sensing theory, an improved orthogonal matching pursuit algorithm is proposed to obtain the high-resolution range-velocity profile while reducing the computation complexity. Finally, several numerical simulations are provided to verify the effectiveness of the proposed method for moving targets. [ABSTRACT FROM AUTHOR]

The height measurement algorithm of metre wave multiple input multiple output (MIMO) radar based on uniform linear array (ULA) has some problems, such as a sharp decline of angle measurement accuracy of ultra‐low altitude targets and large fluctuation of height measurement with the change of elevation. In order to solve the above difficult problems, starting from the perspective of reducing the influence of multipath effect by array structure, coprime array (CPA) is applied to the height measurement of monostatic metre wave MIMO radar. In this paper, the signal model of monostatic CPA metre wave MIMO radar is established under the condition of flat ground, a height measurement method using physical array is proposed based on the derivation that it is infeasible to use virtual array method for height measurement, it fills the gap of low elevation estimation method based on sparse array metre wave MIMO radar. The proposed method calculates the echo data according to the characteristics of the steering vector of CPA, then obtains the accurate low elevation by using the generalised multiple signal classification algorithm, and finally the target height is obtained by using the geometric relationship. Simulation experiments compare three typical CPAs with ULA using equal array elements, and verify the superiority of height measurement performance of monostatic metre wave MIMO radar based on CPA and the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Venus highlands such as Maxwell Montes are an exploration target in the search for a past water‐rich environment. We combine Magellan and Arecibo radar data to characterize surface properties across Maxwell for orbital or landed investigations. Arecibo data reveal a region surrounding and west of the 90‐km crater Cleopatra with lower radar echoes and circular polarization ratio despite increased surface reflectivity. Modeling of microwave emissivity shows that the surface undergoes step‐like reflectivity shifts at ∼6056.2 km and ∼6,061 km radius, such that the changes in radar echo between these altitudes are not due solely to dielectric effects. We propose that fine‐grained Cleopatra ejecta mantles much of Maxwell Montes, consistent with Venus distal crater ejecta patterns and the longevity of mantling debris in the highlands. Future orbital or landed studies must consider the thickness of ejecta, source material, and effects of shock or melting on ejecta mineralogy. Plain Language Summary: Venus may once have had an ocean and perhaps habitable conditions, but the current greenhouse atmosphere is very dense, hot, and dry. New spacecraft missions to Venus will look through the clouds and probe the deep atmosphere to search for evidence of rock types that form when water is abundant, such as those that make up Earth's continents. The best places to look for these rocks are in ancient mountain ranges, but there are many impact craters on Venus that spread thin layers of fine‐grained sediment over vast regions. Earth‐based radar observations provide a unique type of information that maps these sediments as a guide to searches for evidence of a water‐rich past. We show that much of Maxwell Montes, the tallest mountain range on Venus, is at least partly covered by impact‐produced sediments that must be considered when analyzing future orbital observations. Key Points: Mountain ranges on Venus may hold evidence of a past water‐rich periodImpact craters can widely distribute fine‐grained ejecta that masks surface characteristicsMaxwell Montes, the highest range on Venus, has extensive fine‐grained ejecta deposits from Cleopatra crater [ABSTRACT FROM AUTHOR]

Interferometric imaging radar altimeter (InIRA) integrating synthetic aperture radar (SAR) and synthetic aperture radar interferometry (InSAR) is a new type of radar altimeter. InIRA will be widely adopted in marine scientific research and surface water topography measurements. Owing to the geometric distortion of InIRA images, there are difficulties in feature point extraction and matching, making InIRA registration challenging. Here, an improved SAR‐scale invariant feature transform (SIFT, SAR‐SIFT) algorithm is proposed to address these difficulties. First, a modified Harris non‐linear scale space is constructed using the Harris function and non‐linear scale space, and extracted the feature points according to the SAR‐SIFT algorithm. Second, the orbital parameters of InIRA are used to calculate the geographic coordinates of each feature point by Hermit interpolation. Subsequently, a geographic‐coordinate‐based method is proposed to match the feature points. The results show that the proposed algorithm gained more correct matches and improved the registration accuracy by 13% and 16.7% compared with the SIFT and SAR‐SIFT algorithms, respectively, and was 18% more time efficient than the SAR‐SIFT algorithm. The experiments demonstrate that the proposed algorithm outperforms the SAR‐SIFT algorithm for InIRA image registration in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Based on an integrated design of metasurface units and antenna elements, a 2‐bit metasurface array antenna with broadband low radar cross‐section (RCS) covering large incident angle space is proposed in this study. The slot width's influences on the reflective phase and element radiation performance are analysed. Furthermore, a genetic algorithm is utilised to obtain the coding pattern of the array antenna with diffuse scattering characteristics. A wideband RCS reduction is achieved from 4.2 to 9.0 GHz under x‐polarised incidence, and it has oblique incident angle stability ranging from 0° to 60° due to non‐uniform element arrangement. The current cancelation phenomenon is found to reveal the RCS reduction mechanism. Besides, the proposed antenna gain is 15.9 dBi with a peak aperture efficiency of up to 90.8%. Experimental results have a close coincidence with the simulations, which verifies the capability of the proposed design. [ABSTRACT FROM AUTHOR]

This paper describes a technique for identifying hydrometeor particle types using airborne HIAPER Cloud Radar (HCR) and High Spectral Resolution Lidar (HSRL) observations. HCR operates at a frequency of 94 GHz (3 mm wavelength), while HSRL is an eye‐safe lidar system operating at a wavelength of 532 nm. Both instruments are deployed on the NSF‐NCAR HIAPER aircraft. HCR is designed to fly in an underwing pod and HSRL is situated in the cabin. The HCR and HSRL data used in this study were collected during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES). Comprehensive observations of the vertical distributions of liquid and mixed‐phase clouds were obtained using in situ probes on the aircraft and remote sensing instruments. Hydrometeor particle types were retrieved from HCR, HSRL, and temperature fields with a newly developed fuzzy logic particle identification (PID) algorithm. The PID results were validated with in situ measurements collected onboard the HIAPER aircraft by a 2D‐Stereo (2D‐S) cloud probe. Particle phases derived from the PID results compare well with those obtained from the 2D‐S observations and agree in over 70% of cases. Size distributions are also consistent between the two methods of observation. Knowledge of the particle type distribution gained from the PID results can be used to constrain microphysical parameterizations and improve the representation of cloud radiative effects in weather and climate models. Key Points: A fuzzy logic hydrometeor particle identification method is presented that uses airborne cloud radar and lidar observationsThe method classifies cloud and precipitation particles into eleven types of different phases and sizesA comparison of hydrometeor classifications with in situ measurements of Southern Ocean clouds show agreement in over 70% of cases [ABSTRACT FROM AUTHOR]

When micro‐Doppler (MD) radars are distributed, a federated learning strategy over wireless backhaul links is developed for motion classification. Specifically to identify the human motion, a common convolutional neural network (CNN) model is shared for all the distributed radars (i.e. clients) and it is trained through the federated learning strategy over wireless backhaul connected to the main server. In the proposed system, a main bottleneck is the estimation of local gradients for CNN training at the server, which are transferred from distributed radars over the wireless backhaul link. To overcome it, a deep learning (DL) aided gradient estimation algorithm is proposed, in which the deep neural networks (DNNs) for encoding local gradient vectors at the distributed radars and the DNN for decoding (i.e. estimating) them at the server are jointly trained in an end‐to‐end autoencoder‐based learning strategy. To avoid the inter‐client interference over the wireless backhaul link, the DNN structure for the gradient estimation algorithm with the orthogonal multiple access is proposed, in which the proposed DNN effectively learns the encoding/decoding at the transceiver over wireless backhaul. By exploiting the experimental data measured through the USRP‐based MD radars, the authors validate the motion classification performance of the proposed federated learning strategy and DL aided gradient estimation over the wireless backhaul link. [ABSTRACT FROM AUTHOR]

The purpose of operating a radar system multistatically is to acquire information about targets of interest more quickly and reliably than would be possible using disparate radars, which fuse data and track post‐detection. This paper identifies and addresses the critical issues for designing a coherent multistatic radar system using conventional coherent radars and proposes a set of design principles for configuring a coherent multistatic radar network with the aim of maximising the utility of information while constraining system cost and complexity. The issues discussed are as follows: the localisation of targets in three dimensions; eliminating the inevitable spatial ambiguities arising from sparse spatial sampling while exploiting the large aperture for high resolution; the achievement of spatial coherent processing gain for target detection; the effect of phase centre localisation, phase and frequency errors on synchronisation; the incorporation of three‐dimensional Doppler estimation for slow‐time coherent integration. The arguments presented are physically‐based and complemented by mathematical analysis and numerical experimentation using non‐linear sparse estimation, the sparse algorithm being critically important for resolving spatial ambiguities. An optimal configuration is identified, which has sufficiently many nodes to overcome spatial ambiguities and achieves significant spatial coherent processing gain, while simultaneously constraining the overheads of inter‐node synchronisation and communication. [ABSTRACT FROM AUTHOR]

As one of the key technologies of electromagnetic spectrum operations, radar waveform recognition is an important basis for judging the threat degree of enemy's weapons. At present, it is difficult to recognize the waveform at low signal‐to‐noise ratio (SNR), and the theoretical simulation is used in most research studies, which leads to the problem that the engineering practicality of the algorithm is poor. In this paper, a two‐stream convolutional network radar waveform recognition (TCNRWR) algorithm that fuses the original time–frequency image and the continuous frames autocorrelation time–frequency image is proposed, and an experimental platform is built by using software defined radio technology. Compared with traditional theoretical simulations, this paper focusses on the influence of spatial background interference and signal incompleteness on the recognition of intra‐pulse signals, and proposes an automatic removal method of invalid signal in the time–frequency domain. In order to further improve the recognition rate at low SNR, a deeper feature of short‐time autocorrelation flow is excavated, and a two‐stream time–frequency images fusion convolutional network (TTIFCNet) is constructed. The network effectively fuses the pre‐classification results of the two streams through Softmax layer and different weights. Finally, the hardware platform HackRF is used to realize the communication and processing of signals in real space, which proves the effectiveness of the TCNRWR algorithm. [ABSTRACT FROM AUTHOR]

A multiplatform radar network (MRN) composed of multiple transmitting and receiving facilities is advantageous over monostatic radar in moving target detection (MTD) and tracking due to the diversity gain. Within the framework of the Internet of Battlefield Things (IoBT), the authors propose that the airborne platforms equipped with the cognitive multimodal radar (CMR), the subarray‐based MIMO radar (SMR), the active electronically scanned array (AESA) radar, and the adaptive radar countermeasures (ARC) should team up in target detection and tracking. To obtain better target detection and tracking performance, the probing signals from the CMR, the SMR, and the AESA radar are adaptively optimised based on the scenario under consideration. The ARC consists of two parts: 1) RF signal sensing and recognition with deep neural network (DNN), that is, to detect, identify, and localise multiple radio frequency emitters; 2) intelligent fake return creation and jamming to confuse the enemy radars in real time in the field. This work aims to serve as a good reference for future researchers interested in developing MRN with diverse nodes and the general teaming of radars on the battlefield. [ABSTRACT FROM AUTHOR]

Considering the signal model of the frequency diverse array (FDA), the amplitude and phase characteristics cannot be ignored. The beampattern mainly reflects the amplitude characteristics. Existing investigations show that the angle‐range dependence of the beampattern offers the FDA several benefits regarding detection and location. However, for the FDA, the phase characteristics that have not been studied are significant when considering the imaging and anti‐jamming. In this work, the authors investigate the phase characteristics of the FDA based on the proposed generalised FDA structure. Then, based on the structure, the authors derive the general closed form of the signal model, the phase‐radiation function, and the phase‐radiation pattern. Moreover, the periodicity of the phase radiation pattern is analysed using the recursive algorithm as compared with the rational analysis. Finally, using the three‐point method, the phase centre of the generalised FDA is derived. Furthermore, the proposed signal model and the derived phase characteristics of the FDA are verified by extensive numerical results. [ABSTRACT FROM AUTHOR]

Intra‐abdominal hypertension (IAH) has gained increasing attention worldwide because of its prevalence and high mortality rate among intensive care unit (ICU) patients. Most current approaches of measuring intra‐abdominal pressure (IAP) involve the use of sensors inside or attached to the body, which may, however, make daily monitoring inconvenient. This paper proposes a noninvasive and contactless system to learn the relationship between the passive mechanical behavior of the abdominal wall with millimeter‐wave (mm‐wave) frequency‐modulated continuous wave (FMCW) radar and an IAP sensor via a deep learning approach. We correlated the IAP variance with the mobility measures of the abdominal wall and proposed Pearson‐coefficient‐guided domain adversarial neural network (PCG‐DANN) to learn the mapping relationship. To validate the efficacy of our proposed method, a stable intra‐abdominal hypertension/abdominal compartment syndrome (IAH/ACS) model using swine was established to evaluate the mobility of the abdominal wall under different intra‐abdominal pressures with multichannel mm‐wave FMCW radar. The superiority of the proposed method is demonstrated by comparing with other neural network structures and mostly adopted sensor‐based methods. These preliminary results confirm that the new methodology for evaluating IAP nonlinearity is promising and that it can serve as an important diagnostic and treatment reference for patients with IAH. [ABSTRACT FROM AUTHOR]

This study investigates warm‐season extreme precipitation events (EPEs) with the focus on the less‐attended weak convection (WeEPEs) using spaceborne radar observations. WeEPEs are compared to EPEs with intense convection (InEPEs), including their frequency/location, convective structures, and storm environments. WeEPEs and InEPEs both account for about 30% of the EPEs, highlighting the importance of WeEPEs. Overland WeEPEs mainly occur over key monsoon regions and maximize over windward sides of coastal mountains likely due to orographic enhancement. WeEPEs develop under conditions with deep moist profiles and strengthened low‐level onshore flows. Radar, microwave, and lightning proxies all suggest a weak updraft and limited ice contents in WeEPEs. However, vertical radar profiles of WeEPEs exceptionally increase (∼15 dBZ) downward below the melting level, indicating substantial raindrop growth and/or concentration increase through very active warm‐rain processes. This study provides useful guidelines for evaluating high‐resolution model simulations and satellite rainfall retrievals on extreme precipitation. Plain Language Summary: When extreme precipitation occurs, one will easily connect it to intense storms such as those producing numerous lightning flashes. However, some rainstorms display little severe signatures, therefore, they are even more challenging from the monitoring or nowcasting point of view. This study investigates the warm‐season extreme precipitation events (EPEs thereafter) with weak convection (weak upward motion indicated by satellite proxy) in the tropics and subtropics using 15 years of satellite observations. Surprisingly, EPEs with weak convection (WeEPEs) over land are as frequent (∼30%) as EPEs with intense convection, highlighting the significance of WeEPEs. Over land, WeEPEs preferentially develop over deep monsoon regions and their maxima are located over or near coastal mountains, suggesting the important role of orographic lifting/convergence in producing WeEPEs. Spaceborne radar, microwave, and lightning observations all indicate that WeEPEs have a weak convective intensity and limited ice contents above the melting level. WeEPEs are featured by vertical radar profiles persistently increase downward below the melting level (by ∼15 dBZ). Our findings collectively suggest that heavy rainfall in WeEPEs is largely caused by precipitation process in the warm‐cloud layer, instead of the melting of large amount of ice particles. Key Points: Nearly 30% warm‐season extreme precipitation events (EPEs) have only weak convection, similar fraction as EPEs with intense convectionMost weak convective EPEs (WeEPEs) are situated over the ITCZ, Asian Monsoon, and South American Monsoon, especially coastal regionsWeEPEs are featured by limited ice‐phase process and vertical radar profiles markedly increasing downward below the freezing level [ABSTRACT FROM AUTHOR]

This study investigates the use of adaptive radar waveforms in performing a multi‐task operation involving the tracking and classification of a single target. The work applies a previously reported hierarchical fully adaptive radar framework and considers a situation in which the target tracking function is performed using a transmit waveform having adaptive pulse repetition frequency, pulse width, bandwidth, and coherent processing interval parameters, while the classification function can be performed using only a fixed radar waveform. Two alternate strategies for selecting the next waveform are then described. The first strategy is focussed on maintaining fixed measurement performance goals in terms of target range and velocity and classification entropy, while the second approach varies these performance goals depending on the target state. Both approaches are examined for simulated datasets at W‐band representing a moving human target, as well as for laboratory measurements of this scenario. The results for both simulation and experiment show the benefits of including adaptive waveforms in performing multiple radar tasks. [ABSTRACT FROM AUTHOR]

For the existing jamming discrimination methods on the multistatic radar system, the single feature of target echo space correlation is utilised as the metric, which leads to the lack of comprehensive feature extraction and universal discrimination algorithm. In this study, a discrimination method in a multistatic radar system based on the convolutional neural network is proposed. This proposal combines the advantages of multiple‐radar systems cooperative detection technology with the convolutional neural network, and effectively applies to the field of anti‐deception jamming, which takes full advantage of unknown information of echo data to obtain multi‐dimensional, comprehensive, complete and deep feature differences besides correlation, so as to achieve a better jamming discrimination effect. The simulation results show that the proposed method can extract the multidimensional and separable essential features of echoes, and all these features have a strong degree of differentiation between targets and jamming, which effectively reduce the influence of noise and pulse number. At the same time, the influence of radar distribution on jamming discrimination under non‐ideal conditions is relieved, when the correlation coefficient of the true target reaches 0.4, the discrimination probability remains above 85%, which broadens the boundary conditions of the application process. [ABSTRACT FROM AUTHOR]

Increasingly complex electromagnetic environment makes some troubles for a radar system. Improving the performance of the radar detection and suppressing the interferences are important challenges. In this study, joint transmit filters and sparse array design enabling the maximum signal to interference plus the noise ratio (SINR) beamforming at the transmit subaperturing frequency diverse array multiple‐input multiple‐output (TS‐FDA‐MIMO) radar is examined. However, the optimisation problem is non‐convex. A cycle iteration algorithm is proposed to obtain the optimal transmit filters and sparse array. The total problem is divided into two sub‐problems. The first optimisation sub‐problem is the sparse design and the Successive Convex Approximation (SCA) with two‐dimensional reweighted mixed l1, 2‐norm method is introduced to solve the sub‐problem. In addition, the transmit filters design is the second optimisation problem. The sub‐problem is changed to the semi‐definite relaxation (SDR) problem and the Gaussian random method is utilised to obtain the optimal transmit filters. Through the cycle iteration of the two sub‐problems, the optimal solutions are obtained. Finally, the simulation results verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Development of a non-contact 15-second paediatric respiratory rate monitor using microwave radar and its clinical application With the laptop, the PRR-Monitor determines RR and HR, derived from radar-derived cardiorespiratory thoracic motions, in 15 s, and without the laptop, it determines RR in 30 s. Respiratory rate (RR), one of three parameters of sepsis-3, is recognised as an important predictive factor for severity of diseases such as asthma in children.[1] It predicts clinical deterioration more effectively compared with other vital signs.[2] Despite the importance of RR and due to inconvenience of contact-type sensors, medical professionals mostly determine RR by visually counting number of thoracic motions per a minute in outpatient unit, notwithstanding its low measurement accuracy and reproducibility.[3] Even in emergency departments, only 51% of paediatric patients' RR is measured in Europe and only 40% of that is measured in the US[4]. [Extracted from the article]

The problem of high‐resolution direction finding is considered using a distributed array network with array position errors, called partly calibrated array. These errors are common in practice, and significantly affect high angular resolution. Existing calibration methods rely on centralized data transmission, which requires all the measurements to be transmitted to a computation center. This centralized mode leads to communication bottlenecks and system inflexibility. Here, a decentralized mode is designed, where each sensor only communicates locally with their neighboring sensors. Based on this mode, a corresponding direction‐finding approach is proposed using sequential methods that process data one by one, which corresponds to the decentralized signal processing sensor by sensor. Simulations verify that the proposed method achieves decentralization with minor cost in estimation accuracy.We consider high‐resolution direction finding using distributed arrays with position errors between subarrays. Existing methods are based on centralized mode, which has communication bottlenecks and system inflexibility. Here, we propose a decentralized algorithm to solve this problem. [ABSTRACT FROM AUTHOR]

A knowledge of melting layer (ML) is crucial for the accurate estimation of rainfall and the study of microphysical characterization of the cloud. Based on 5 years (2016–2020) of measurements of cyclones over the Western North Pacific from the dual‐frequency precipitation radar on board the Global Precipitation Measurement satellite, the characteristics and microphysical processes of ML are investigated. Our research shows that the height of the top boundary of ML is higher than the freezing level (0 degrees centigrade level, FL), and the Ze of freezing level is larger than that of the top of ML. The height of ML is the result of the combined effect of latitude and stage of cyclone. When the ice particles pass through the ML, the mass‐weighted mean diameter (Dm), the generalized intercept parameter (Nw), and the distributions of the reflectivity factor (Ze) increase, indicating that growth mechanism and breakup process. Key Points: The height of melting layer in the cyclons region is higher than the 0°C isotherm heightThe melting layer in the cyclons region is different in different stages and latitudeThe microphyscics process in near the melting layer [ABSTRACT FROM AUTHOR]

A knowledge of melting layer (ML) is crucial for studying precipitation and hydrometeor. Based on the measurements of dual‐frequency ratio (DFRm) from the dual‐frequency precipitation radar on board the Global Precipitation Measurement satellite, we use the covariance wavelet transform (CWT) method to identify the ML top height (MLTH) through the DFRm profiles. Due to the influence of disturbance, the directly retrieved MLTH could be estimated mistakenly. To improve the reliability of the retrieved MLTH, a quality‐control algorithm based on the CWT method is developed. By comparing the MLTH with the corresponding 0°C isothermal heights, the retrieved MLTH are more reliable than the dual‐frequency precipitation radar (DPR) product, showing great potential in deploying the Global Precipitation Measurement DPR and the CWT method in the identification of the MLTH. Key Points: The profile of dual‐frequency ratio contains the information of the melting layer (ML)The covariance wavelet transform can be used to identify the MLA quality‐control algorithm was developed to improve the reliability of the results [ABSTRACT FROM AUTHOR]

Utilizing the measurements and retrievals from the Global Precipitation Measurement dual‐frequency precipitation radar data during the summer of 2014–2020, this study analyzes the precipitation microphysics for summer rainfall over the Yangtze‐Huai River Valley (YHRV) region in China with respect to the precipitation efficiency index (PEI). The results show that the mean near‐surface rain rate and PEI for convective precipitation are generally larger than that for stratiform precipitation. There are larger raindrops in near‐surface rainfall for convective precipitation than that for stratiform precipitation. The High PEI precipitation is characterized by the larger ratio of convective precipitation samples compared to that for the other two categories of precipitation. The mean mass‐weighted mean diameter (Dm) of the near‐surface raindrops increase along with the precipitation top height (PTH) in Moderate PEI precipitation for convective and stratiform precipitating clouds. The larger PEI indicates the larger occurrence of the heavy raindrops and strong radar echoes near the ground. The growth process of raindrops falling below the melting layer in the High PEI precipitation is dominated by collision‐coalescence while the collisional break up is the dominant process below the melting layer in Low PEI precipitation. It is further verified that the microphysical characteristics and mechanisms of summer rainfall over the YHRV region are similar to that of precipitation derived from tropical cyclones over the ocean during the monsoon period. Key Points: The near‐surface raindrops increase along with the precipitation top height in the moderate precipitation efficiency index (PEI) precipitationThe higher PEI indicates the larger occurrence of near‐surface heavy raindrops and strong radar echoesThe microphysical mechanisms in the summer precipitation process are analyzed in terms of various PEI [ABSTRACT FROM AUTHOR]

The present study introduces the online non‐linear bias correction for the assimilation of all‐sky GOES‐16 Advanced Baseline Imager (ABI) channel 9 (6.9 μm) radiances in a rapidly cycled EnKF for convective scale data assimilation (DA). This study is the first to explore the use of the radar reflectivity as the anchoring observation for ABI all sky radiance assimilation. The online and offline nonlinear bias correction methods are compared and evaluated for a case of rapidly developing supercells over Oklahoma and Texas. The analysis and background of the online bias correction perform better than the offline approach during the suppression of spurious clouds and the establishment of non‐precipitating and precipitating regions when the supercell storms are observed to develop. The online approach not only improves the analysis and background over the radar anchored region but also the unanchored non‐precipitating regions compared to the offline approach. Both quantitative and subjective verification of the deterministic forecasts showed consistent superior performance from the online bias correction over the offline approach. Diagnostics reveal that the online bias correction retains useful information in the innovation, which in turn improves subsequent analysis, background and background ensemble spread for both the thermodynamic and dynamic fields. The effect is accumulated during the DA cycling that is responsible for the superior analysis and forecast of the supercells. Plain Language Summary: The effective assimilation of satellite radiance observations requires removal of observation biases. Past studies have shown the advantages of the online bias correction approach for the clear‐sky radiance data assimilation relative to the offline bias correction approach. The advantage is achieved by simultaneously updating the model states and the bias correction terms and through the assimilation of unbiased observations together with the radiance observations. The anchoring unbiased observation in the online bias correction allows a more effective separation of the observation biases and the model bias. In this study, we propose and implement an online nonlinear bias correction method for the rapid EnKF assimilation of the all‐sky ABI radiances. The radar reflectivity observations are explored as the anchoring observations. Our results and diagnostics from a supercell case study show the online nonlinear bias correction approach improve the analysis and forecast compared to the offline nonlinear bias correction approach. Key Points: An online nonlinear bias correction for Advanced Baseline Imager (ABI) all‐sky radiance assimilation is proposed and implemented in a rapidly updated EnKFRadar reflectivity is shown to be effective in anchoring the state variable update by distinguishing the model and ABI radiance biasesThe online bias correction improves the analysis and forecast for rapidly developing supercells relative to the offline approach [ABSTRACT FROM AUTHOR]

Mutual interference between different radar waveforms used in automotive radar applications is studied. The existing interference analysis is extended to a generalised radar‐to‐radar interference equation that covers most of the common interference scenarios for automotive radar systems. The outcome of the generalised equation is demonstrated for a number of typical scenarios where radars with different continuously transmitting waveforms are involved. The proposed equation can be used to characterise the received interference and its features by analysing the instantaneous beat frequency of the victim radar. Moreover, an interference analysis of phase‐coded frequency‐modulated continuous waveforms is performed and demonstrated experimentally by using real‐time automotive radars for the first time in the literature. The experimental results corroborate the interference analysis of different waveforms and validate the proposed generalised interference equation under various conditions. [ABSTRACT FROM AUTHOR]

This study focusses on the target detection problem in the distributed multiple‐input multiple‐output (MIMO) radar. The observation environment is assumed to be partially homogeneous in one transmitter‐receiver path and simultaneously non‐homogeneous for different paths. By characterising the disturbance signals with a series of auto‐regressive (AR) processes, two parametric detectors based on the Wald test are developed. The first one utilises an iterative scheme to estimate the unknown parameters that were involved in the derivation, and the second one obtains these estimations sequentially. One of the most attractive features of the two distributed MIMO radar detectors is that they can be used in the presence/absence of the training data. More specifically, they can take advantage of the training data to boost the detection performances effectively even if the training data are very limited. The simulation results of several scenarios demonstrate the remarkable detection performances of authors' proposed methods compared with that of the competitors. Furthermore, the amplitude estimation performances of the two detectors are evaluated via the corresponding Cramér–Rao bound (CRB). [ABSTRACT FROM AUTHOR]

The main‐lobe jamming causes serious degradation of radar system performance in practical applications. In recent years, the Blind Source Separation (BSS) method has been applied to suppress the main‐lobe jamming. However, the received echoes of target and jamming cannot be effectively identified while the robustness of the conventional BSS method is poor due to the influence of system noise. To tackle these problems, a radar main‐lobe jamming suppression and identification method is proposed based on robust whitening BSS and Convolutional Neural Networks methods. First, the target echoes and jamming are separated by the proposed robust whitening BSS method. An iterative optimisation method is proposed to obtain the robust whitening matrix, and sufficient conditions are provided for solvability. Then, a Target echo and Jamming Identification Network (TJINet) is proposed to identify the signals after the robust whitening BSS. The proposed TJINet method uses residual blocks and branch structures to improve classification performance and utilises asymmetric convolution blocks to extract subtle features between the target echo and deceptive jamming. Simulation and experimental results show that the proposed method has better jamming suppression performance than the traditional methods under low signal‐to‐noise ratio (SNR) conditions, the algorithm converges faster, and also can stably identify the target echo and jamming signals. [ABSTRACT FROM AUTHOR]

Radar has been shown potentially to be used for non‐contact sensing of biosignals in a more comfortable and easier way than wearable and contact devices. By detecting and extracting body motions linked to physiological activities, accurate estimation of the heart rate is possible. However, the detection and estimation of heartbeat signal in practical applications is disturbed seriously by noise and respiration harmonic signal. In this study, a multi‐level coarse‐to‐fine method is proposed for heartbeat rate (HR) estimation with a frequency‐modulated continuous‐wave radar operating at 60 GHz utilised to extract the heartbeat information of human body under low signal‐to‐noise ratio conditions. First, the frequencies, which are likely to be the HR, are extracted in the coarse estimation part. Then, evaluations about the extracted frequencies are conducted in the fine estimation part. Finally, the multiple signal classification algorithm is utilised to separate the HR frequency from the respiration harmonics, which solves the frequency resolution issue caused by the observation window size, and achieves high accuracy estimation of HR. The experiments on measured data demonstrate the effectiveness of the proposed method. Compared with the state of the art, the proposed method has superiority in terms of estimation accuracy of HR. [ABSTRACT FROM AUTHOR]

Two years (2018–2019) of different precipitation products are assessed for their skill in capturing orographic precipitation over the western United States (30°–50°N & 105°–135°W) using two popular methods. The first method defines orographic indices using orographic enhancement and moisture content that represents the amount of moisture advected over sloping terrain. In contrast, the second method classifies precipitation events into orographic and non‐orographic events. NCEP Stage‐IV product is used as a reference. All of the evaluated products (radar, microwave imager/sounder, and infrared) show more significant errors for the orographic than the non‐orographic events. The Global Precipitation Mission (GPM) products; Dual‐frequency Precipitation Radar (DPR), combined radar and radiometer (COMBINE), and GPM Microwave Imager (GMI), and Microwave Humidity Sounder (MHS), severely underestimate the precipitation rates, especially for heavy precipitation (>4 mm/day), whereas infrared precipitation (used in the Integrated Multi‐satellite Retrievals for GPM; IMERG‐IR) and a reanalysis product (ERA5) show relatively better estimation. It was found that the amount of precipitation over the reference (rate BIAS) varies with seasons, so in cold seasons satellite precipitation products tend to underestimate while in warm‐season they (except DPR) tend to overestimate precipitation amount. Most of the satellite products severely underestimate precipitation volume at relatively colder surfaces (<10°C) and lower TPW (<15 mm), but ERA5 shows little rate BIAS in such cases. The underestimation tends to be larger for orographic than non‐orographic events. In contrast, ERA5 shows relatively large underestimation at warmer temperatures (>20°C), where satellite products tend to overestimate precipitation amounts. Key Points: Several satellite‐based precipitation products are compared in relation with orographic enhancementSatellite‐based precipitation BIAS shows a seasonal patternPrecipitation BIAS is associated with the surface temperatures and total precipitable water [ABSTRACT FROM AUTHOR]

In the complex electromagnetic environment of cities, detection and positioning of slow and small unmanned aerial vehicles (UAVs) have always been a very challenging research topic. Usually, multiple radar observation stations are required to detect and track targets. Moreover, each radar observation station requires strict synchronisation, and the system is complex and costly. In view of this, under the condition of known prior information of urban geography, this study proposes a method to locate UAVs in complex electromagnetic environments, which use multiple virtual mirror synchronous radars constructed by multipaths and created by the complex buildings in urban environments. First, the simulation model is constructed, and the corresponding simulation parameters are given. Then, the algorithm for solving the single‐station radar location is proposed, and the algorithm is verified by the simulation of a ray propagation model. Finally, the proposed algorithm is further verified based on the measured data captured by the radar system in Section 2. The results show that the proposed algorithm based on the single‐station radar is able to locate the UAV in both line‐of‐sight and non‐line‐of‐sight site, and its positioning accuracy is better than 1 m. [ABSTRACT FROM AUTHOR]

The multipath effect deteriorates the performance of low elevation target altitude estimation with a ubiquitous radar strongly, especially in complex terrain environments in which multiple reflection paths exist simultaneously. This work shows how the authors reconstructed a multipath model for ubiquitous radar with a known waveform and took advantage of it to improve the altitude measurement performance when locating low‐altitude small targets in complex terrain environments. The authors first present a multipath propagation model for target elevation angle estimation in which not only received multipaths but also transmitted multipaths are taken into consideration. Then, a reconstructed elevation angle estimation model in which the waveform is known and the target elevation information is as same as the original model is derived. Finally, an innovative low elevation angle estimation algorithm based on a maximum likelihood estimator and a sparse representation is proposed. Simulations and experiment results verify that the proposed algorithm has a much more robust low elevation angle estimation performance than existing methods in complex terrain environments. Index Terms—Target altitude measurement, complex multipath effect, known transmitted waveform, sparse representation, ubiquitous radar. [ABSTRACT FROM AUTHOR]

In this study, the joint design of discrete phase transmit sequences and receive filters with the low peak sidelobe level and integrated sidelobe level in radar systems is considered. The problem is formulated as a minimisation of the peak (integrated) sidelobe level, forcing a unimodular discrete phase constraint at the design stage. To deal with the resulting non‐convex and in general the NP‐hard optimisation problem, an iterative method based on the alternate optimisation approach is proposed. Each iteration of the proposed method involves a non‐convex min–max problem to be solved. In addition, a sequential rank‐one constraint relaxation framework is proposed to initialise the procedure, each iteration of which requires the solution of a convex problem. Simulation results show that using the proposed framework for jointly finding transmit sequences and receive filters leads to a better peak sidelobe ratio and integrated sidelobe ratio for discrete phase sequences than the optimal mismatched filters design approach. [ABSTRACT FROM AUTHOR]

Traffic participant classification is critical in autonomous driving perception. Millimetre wave radio detection and ranging (RADAR) is a cost‐effective and robust means of performing this task in adverse traffic scenarios such as inclement weather (e.g. fog, snow, and rain) and poor lighting conditions. Compared to commercial two‐dimensional RADAR, the new generation of three‐dimensional (3D) RADAR can obtain height information about targets as well as their dense point clouds, greatly improving target classification capabilities. This study proposes a multi‐objective classification method for traffic participants based on 3D RADAR point clouds. First, a 22‐dimensional feature vector of the 3D RADAR point cloud distribution was extracted to describe the shape, discrete, Doppler, and reflection intensity features of the targets. Then, dynamic and static datasets containing five classes of targets were produced, creating a 10k frame. Extensive experiments were conducted to build machine learning classifiers. The experimental results show that the trained classifiers can achieve over 92% classification accuracy when the targets are classified into five groups and over 95% classification accuracy when the targets are classified into four groups. The proposed method can guide the design of safer and more efficient intelligent driving systems. [ABSTRACT FROM AUTHOR]