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With the development of electronic warfare, anti‐jamming measure becomes more and more complex. There have been certain research results on jamming strategies, but only a few research materials on anti‐jamming strategies. It is difficult to simulate the real jamming environment, and there is no appropriate anti‐jamming decision‐making model for research. Cognitive radar can perceive the environment and receive feedback, which provides the possibility to solve the problem of anti‐jamming decision‐making. This article regards the anti‐jamming measure as a kind of interaction behaviour and establishes the cognitive radar antagonistic environment model and uses the reinforcement learning algorithm to solve the problem of anti‐jamming decision‐making. Finally, this article verifies the feasibility of applying reinforcement learning theory on making anti‐jamming decision in the radar antagonistic environment model. The performance of different reinforcement learning algorithms is compared, and their advantages and disadvantages are discussed. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles, or drones, pose a significant threat to privacy and security. To understand and assess this threat, classification between different drone models and types is required. One way in which this has been demonstrated experimentally is through this use of micro‐Doppler information from radars. Classifiers capable of exploiting differences in micro‐Doppler spectra will require large amounts of data but obtaining such data experimentally is expensive and time consuming. The authors present the methodology and results of a drone micro‐Doppler simulation framework which uses accurate 3D models of drone components to yield detailed and realistic synthetic micro‐Doppler signatures. This is followed by the description of a purpose‐built validation radar that has been developed specifically to gather high‐fidelity experimental drone micro‐Doppler data with which is used to validate the simulation. Detailed comparisons between the experimental and simulated micro‐Doppler spectra from three models of drones with differently shaped propellers are given, showing very good agreement. The aim is to introduce the simulation methodology. Validation using single propeller micro‐Doppler is provided, although the simulation can be extended to multiple propellers. The simulation framework offers the potential to generate large quantities of realistic drone micro‐Doppler signatures for training classification algorithms. [ABSTRACT FROM AUTHOR]

For the integration detection of near space hypersonic weak targets by high pulse repetition frequency (PRF) radar, a novel method named Multi‐Hypothesis Fuzzy‐Matching Radon transform (MHFM‐RT) is proposed for the near space hypersonic target detection and tracking. For remote hypersonic target detection, to avoid range ambiguity, current radars always use a low PRF mode, which limit the number of pulse accumulations. Using the high PRF mode, the fuzzy folding will appear in the target range measurements when target trajectory crosses range fuzzy intervals. Therefore, there is a contradiction between range ambiguity and energy accumulation. The proposed method is used to match the fuzzy measurements, so as to realise the correct integration in the condition of range ambiguity. Firstly, considering the need of range ambiguity resolution, the mode of staggered PRF is used. Secondly, the first frame measurements are periodically extended for multiple‐fuzzy hypothesis. Finally, the weak target track is accumulated in MHFM‐RT domain, and the signal integration and ambiguity resolution can be realised simultaneously. The proposed method expands the Variable‐Diameter‐Arc‐Helix Radon transform (VDAH‐RT) method to fuzzy folding conditions. Compared with the existing methods, for 7‐scan measurements non‐coherent integration, the detection sensitivity of the proposed method is about 0.5–1 dB higher than that of the IMM hybrid filter algorithm, and about 1 dB higher than that of the RHT‐TBD approach, and it needs less storage space and has higher detection probability. [ABSTRACT FROM AUTHOR]

Rainfall frequency analysis methods are developed and implemented based on high‐resolution radar rainfall data sets, with the Baltimore metropolitan area serving as the principal study region. Analyses focus on spatial heterogeneities and time trends in sub‐daily rainfall extremes. The 22‐year radar rainfall data set for the Baltimore study region combines reflectivity‐based rainfall fields during the period from 2000 to 2011 and polarimetric rainfall fields for the period from 2012 to 2021. Rainfall frequency analyses are based on non‐stationary formulations of peaks‐over‐threshold and annual peak methods. Increasing trends in short‐duration rainfall extremes are inferred from both peaks‐over‐threshold and annual peak analyses for the period from 2000 to 2021. There are pronounced spatial gradients in short‐duration rainfall extremes over the study region, with peak values of rainfall between Baltimore City and Chesapeake Bay. Spatial gradients in 100‐year, 1 hr rainfall over 20 km length scale are comparable to time trends over 20 years. Rainfall analyses address the broad challenge of assessing changing properties of short‐duration rainfall in urban regions. Analyses of high‐resolution rainfall fields show that sub‐daily rainfall extremes are only weakly related to daily extremes, pointing to difficulties in inferring climatological properties of sub‐daily rainfall from daily rainfall analyses. Changing measurement properties are a key challenge for application of radar rainfall data sets to detection of time trends. Mean field bias correction of radar rainfall fields using rain gauge observations is an important tool for improving radar rainfall fields and provides a useful tool for addressing problems associated with changing radar measurement properties. Key Points: Rainfall frequency analysis tools based on long‐term, high‐resolution radar rainfall fields are developedSub‐daily rainfall extremes for the Baltimore study region exhibit increasing trends over 22‐year period of recordSub‐daily rainfall extremes exhibited pronounced spatial heterogeneities over the Baltimore study region [ABSTRACT FROM AUTHOR]

Meteor radar observations provide wind data ranging from 80 to 100 km altitude, while the Michaelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) onboard the Ionospheric Connection Explorer satellite offers wind data above 90 km altitude. This study aims to generate wind profiles in the mesosphere and lower thermosphere by combining the winds derived from meteor radar and MIGHTI observations over the Korean Peninsula from January 2020 to December 2021. The wind profiles derived from the two instruments are continuous at night, but they show discrepancies during the day. The atomic oxygen 557.7 nm (green line) emission intensity measured by MIGHTI peaks at approximately 100 km during the day and 94 km at night. The vertical gradient of the airglow volume emission rate is more pronounced during the day. These differences can cause day‐night differences in the MIGHTI wind retrieval accuracy, potentially leading to discrepancies during the day. Plain Language Summary: This study aims to derive vertical wind profiles in the mesosphere and lower thermosphere (MLT) by integrating wind measurements from different techniques. Neutral winds in the MLT provide a means to study the activity of various atmospheric waves originating from the lower thermosphere and their propagation to the upper thermosphere. The Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) instrument onboard the Ionospheric Connection Explorer satellite provides wind measurements above 90 km altitude. A meteor radar in Korea provides wind data in the altitude range of 80–100 km. By combining the MIGHTI and meteor radar observations, we derive extended wind profiles in the MLT. While nighttime winds driven from two different techniques show good agreement, discrepancies exist in daytime winds. Vertical gradients in airglow intensity can affect the wind retrieval from MIGHTI's airglow measurement, and this factor can be one of the sources of daytime discrepancy. Key Points: Vertical wind profiles above 80 km are derived from meteor radar and Ionospheric Connection Explorer (ICON) observations over KoreaThese observations produce continuous wind profiles at night, but discontinuity exists between two measurements during daytimeSignificant vertical variation in airglow intensity on the dayside can impact wind retrieval from ICON airglow observations [ABSTRACT FROM AUTHOR]

Understanding the diurnal variation of horizontal wind in the atmospheric boundary layer is important for weather and climate research and wind energy applications. Here we analyze the hourly data from 91 wind profiler radar sites in China and observe that the power spectral density of horizontal wind in lower troposphere approximately follows the −5/3 power law in the mesoscale range over the ocean and coastal areas. However, in inland areas, the slopes of the power spectra are significantly greater than −5/3. We characterize the temporal and spatial variations of maximum wind speed and low level jets and find that the thermal wind effect may partially contribute to the high percentage of low‐level jets observed in the southeastern coast of China and Hainan Island. While the ERA5 reanalysis reproduces wind spectrum well for time scales >1 day, its spectrum diverges significantly from that of profiler data at shorter time scales. Plain Language Summary: Understanding how the horizontal wind changes throughout the day in the lower part of the atmosphere is important for studying weather and climate and using wind energy. In this study, we looked at data from 91 radar sites in China that measure wind every hour. We found that the pattern of how the wind changes follows a specific mathematical relationship, called a power law, where the wind decreases in a particular way with decreasing spatial and temporal scales. This pattern holds true mostly over the ocean and coastal areas, but in areas further inland, the wind behaves a bit differently. We also studied how the low‐level jet (LLJ), which are fast winds at low altitudes, change over time and space. We discovered that the difference in temperature across the region contributes to the occurrence of these LLJs in coastal areas in southeast China and Hainan Island. Finally, we compare our findings with the ERA5 reanalysis, which demonstrates excellent agreement in reproducing the wind spectrum for time scales greater than 1 day. However, the spectrum derived from the ERA5 reanalysis diverges significantly from the profiler data at shorter time scales. Key Points: Wind spectrum density from wind profiler radars over China shows a less negative slope over inland sites than the −5/3 power law over oceanTemporal and spatial variation of maximum wind speed in the lower troposphere and low level jets are characterizedWind spectrum from ERA5 reanalysis is realistic (deficient) for time scales >1 day (<1 day) compared with profiler data [ABSTRACT FROM AUTHOR]

Multibiomarker studies are useful to evaluate the early warning signs of environmental degradation, and their unified responses are often assessed through two common indexes, Integrated Biomarker Response (IBR) and Integrated Biological Responses version 2 (IBRv2). The R package IBRtools allows users to calculate both IBR and IBRv2 while simultaneously incorporating all the biomarkers under evaluation. The package includes functions for calculating the indexes IBR and IBRv2 and obtaining their standardized values, as well a function for radar chart creation and three example datasets. Here we describe the main algorithms involved in IBR and IBRv2 calculations, a description of the novel package and illustrate a workflow using data examples available on the package to guide the user on how to accurately acquire the values for either the IBR index or the IBRv2 index. The IBRtools package provides a user‐friendly platform for R users to obtain IBR index and IBRv2 values, making it straightforward even for large datasets. [ABSTRACT FROM AUTHOR]

Building on previous radar‐based human activity recognition (HAR), we expand the micro‐Doppler signature to 6 domains and exploit each domain with a set of handcrafted features derived from the literature and our patents. An adaptive thresholding method to isolate the region of interest is employed, which is then applied in other domains. To reduce the computational burden and accelerate the convergence to an optimal solution for classification accuracy, a holistic approach to HAR optimisation is proposed using a surrogate model‐assisted differential evolutionary algorithm (SADEA‐I) to jointly optimise signal processing, adaptive thresholding and classification parameters for HAR. Two distinct classification models are evaluated with holistic optimisation: SADEA‐I with support vector machine classifiers (SVM) and SADEA‐I with AlexNet. They achieve an accuracy of 89.41% and 93.54%, respectively. This is an improvement of ∼11.3% for SVM and ∼2.7% for AlexNet when compared to the performance without SADEA‐I. The effectiveness of our holistic approach is validated using the University of Glasgow human radar signatures dataset. This proof of concept is significant for dimensionality reduction and computational efficiency when facing a multiplication of radar representation domains/feature spaces and transmitting/receiving channels that could be individually tuned in modern radar systems. [ABSTRACT FROM AUTHOR]

Radar‐based human activity recognition is considered as a competitive solution for the elderly care health monitoring problem, compared to alternative techniques such as cameras and wearable devices. However, raw radar signals are often contaminated with noise, clutter, and other artifacts that significantly impact recognition performance, which highlights the importance of prepossessing techniques that enhance radar data quality and improve classification model accuracy. In this study, two different human activity classification models incorporated with pre‐processing techniques have been proposed. The authors introduce wavelet denoising methods into a cyclostationarity‐based classification model, resulting in a substantial improvement in classification accuracy. To address the limitations of conventional pre‐processing techniques, a deep neural network model called Double Phase Cascaded Denoising and Classification Network (DPDCNet) is proposed, which performs end‐to‐end signal‐level classification and achieves state‐of‐the‐art accuracy. The proposed models significantly reduce false detections and would enable robust activity monitoring for older individuals with radar signals, thereby bringing the system closer to a practical implementation for deployment. [ABSTRACT FROM AUTHOR]

The authors present multiple machine learning‐based methods for distinguishing maritime targets from sea clutter. The main goal for this classification framework is to aid future millimetre wave radar system design for marine autonomy. Availability of empirical data at this frequency range in the literature is scarce. The classification and anomaly detection techniques reported here use experimental data collected from three different field trials from three different millimetre wave radars. Two W‐band radars operating at 77 and 94 GHz and a G‐band radar operating at 207 GHz were used for the field trial data collection. The dataset encompasses eight classes including sea clutter returns. The other targets are boat, stand up paddleboard/kayak, swimmer, buoy, pallet, stationary solid object (i.e. rock) and sea lion. The Doppler signatures of the targets have been investigated to generate feature values. Five feature values have been extracted from Doppler spectra and four feature values from Doppler spectrograms. The features were trained on a supervised learning model for classification as well as an unsupervised model for anomaly detection. The supervised learning was performed for both multi‐class and 2‐class (sea clutter and target) classification. The classification based on spectrum features provided an 84.3% and 80.1% validation and test accuracy respectively for the multi‐class classification. For the spectrogram feature‐based learning, the validation and test accuracy for multi‐class increased to 93.3% and 88.7% respectively. For the 2‐class classification, the spectrum feature‐based training accuracies are 88.1% and 86.8%, whereas with the spectrogram feature‐based model, the values are 95% and 94.1% for validation and test accuracies respectively. A one class support vector machine was also applied to an unlabelled dataset for anomaly detection training, with 10% outlier data. The cross‐validation accuracy has shown very good agreement with the expected anomaly detection rate. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Radar, Sonar & Navigation. It discusses the advances in AI-assisted radar sensing applications and the challenges that hinder its adoption in this field. The special issue of the journal features nine papers that address these challenges and offer innovative ideas and experimental results. The papers cover a range of topics, including health monitoring, human activity recognition, voice identification, elderly care health monitoring, track-to-track association, signal pre-processing, traffic congestion alleviation, and target recognition. The authors express their gratitude to the contributors and reviewers and believe that the research presented will inspire further exploration and innovation in this field. [Extracted from the article]

Millimetre‐wave radar has been widely used in health monitoring and human activity recognition owing to its improved range resolution and operation in a variety of environmental conditions. With the MIMO antenna array, 4D radar is increasingly employed in autonomous driving, while its application in assisted living is recent and therefore the value added compared to the increase in signal processing and hardware requirements is still an open question. A model for 4D Time‐division multiplexing (TDM) multiple‐input‐multiple‐output (MIMO) frequency‐modulated Continuous wave radar is established using the human activities from the HDM05 motion capture dataset. The simulator produces an end‐to‐end simulation, including four human motions (jumping Jack, kick, punch, and walk), signal time of flight, noise, MIMO signal processing, and classification. Different pre‐processing and point cloud‐based methods are compared to obtain an average classification accuracy of 90% with PointNet. This study simulates a specific 4D TDM MIMO radar configuration to benchmark signal pre‐processing algorithms, which can also assist other researchers to generate range‐Doppler‐time (range‐Doppler time) point cloud data sets for human activities testing different radar configurations, array configurations, and activities saving valuable time in human resources and hardware development before prototyping to assess expected performances. [ABSTRACT FROM AUTHOR]

The millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system. [ABSTRACT FROM AUTHOR]

In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power. [ABSTRACT FROM AUTHOR]

Multipath interference in radar signals caused by sea, ground, and other environments poses significant challenges to the target detection, tracking, and classification capabilities of radar systems. Existing methods for radar signal identification require labeled samples and focus mainly on the classification of normal signals. However, in practice, anomalous samples (multipath interference signals) may be scarce and highly imbalanced (i.e., mostly normal samples). To address this problem, we propose a deep anomaly detection with attention (DADA) for semisupervised detection of multipath radar signals. The method transforms radar signals into time–frequency images and is trained exclusively on normal samples. The autoencoder architecture is extended with a feature extractor network to capture latent sample features. CBAM attention is introduced to improve feature extraction. By learning the distribution of normal samples in high-dimensional image space and low-dimensional feature space, a two-dimensional feature space representing normal samples is constructed. A one-class SVM then learns the boundary of normal samples for anomaly detection. Extensive experiments on radar signal datasets validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A fundamental challenge in the practical implementation of multistatic radar systems (MSRS) is the requirement for precise time and frequency synchronisation between the spatially separated radar nodes. The authors evaluate the performance of different classes of commercially available Global Navigation Satellite Systems (GNSS) timing receivers, Local Oscillators (LO) and GNSS Disciplined Oscillators (GNSSDOs) to determine the limitations of using one‐way GNSS Time and Frequency Transfer (TFT) in this application. From evaluating the performance of three pairs of GNSSDOs, it is concluded that one‐way GNSS TFT will likely be suitable only for the synchronisation of fully spatially coherent MSRS with carrier frequencies up to 100 MHz and waveform bandwidths up to 20 MHz. Whereas, in the case of short‐term spatially coherent MSRS, synchronisation of systems with carrier frequencies up to a few GHz and waveform bandwidths of over 100 MHz will likely be possible. The performance of the different classes of GNSSDOs during GNSS denial (holdover) are evaluated, where it is concluded that frequency offsets between LOs at the point of GNSS denial will often significantly contribute, or even dominate, the holdover performance. Analysis of two practical multistatic radar measurements verifies the function of using the GNSSDOs for wireless synchronisation of the ARESTOR MSRS. [ABSTRACT FROM AUTHOR]

This article is a guest editorial for the IET Radar, Sonar & Navigation journal, focusing on selected papers from the RADAR 2022 conference held in Edinburgh, UK. The conference provided an opportunity for radar specialists from 22 countries to explore the latest developments in radar systems. Key topics discussed at the conference included new radar trends, target detection (with a focus on drones), low-frequency radar, and cognitive radar. The special issue contains 17 papers based on extended work presented at the conference, covering topics such as multistatic radar, passive radar, target signatures, and advanced radar processing techniques. The authors hope that this special issue will serve as a valuable resource for further research in the field. [Extracted from the article]

The presence and importance of small drones (unmanned aerial vehicles) are increasing. With traditional radar detection the target radar cross section and its statistical properties are needed in order to design the detector and to assess detection performance. The authors make a statistical analysis of the radar cross section of two small fixed‐wing drones using measured data from typical flights. Using data from a typical flight instead of measurements or electromagnetic calculations as a function of aspect angle is a convenient method. Work results of the authors in mean values, medians and standard deviations, in analyses of common target probability distributions and in decorrelation times for the radar cross section, are necessary for design and assessment of the detection performance. The authors suggest a new target probability distribution which agrees better with the measured data than the others tested. The authors also note significant differences between the drones. [ABSTRACT FROM AUTHOR]

An analysis of the parameter estimation uncertainty for the target location and velocity achievable using a single‐transmitter‐multiple‐receiver multistatic radar system is presented. A framework for establishing measures of multistatic radar parameter uncertainties by expansion of the bistatic radar parameter uncertainty measures is presented for systems containing omnidirectionally radiating nodes. The methodology uses analytical methods based on the Cramér–Rao Lower Bounds applied to scenarios in a two‐dimensional physical space with a single target exhibiting Doppler characteristics and a bistatic angle dependent radar cross‐section. A set of geometric descriptors is proposed to characterise the system, and parameter uncertainty results are reported as a function of these descriptors. The results indicate that angular separation between the transmitter and the centre of the receiver distribution is of greater importance than the quantity of receivers within the system when low uncertainty estimation capabilities are desired, though a minimum of two receivers must be available. The proportion of receivers within the system which contributed information crucial to obtaining the minimum estimation uncertainty is reported for systems containing different quantities of receivers. It was observed that, as the total number of receivers available increased, the proportion of receivers required to achieve the minimal uncertainty level reduces significantly. [ABSTRACT FROM AUTHOR]

The use of WiFi signals for sensing purposes has attracted a lot of interest from both the radar and communications communities and several techniques have been explored. In the attempt of meeting the requirements for small sensor size, compactness, and easy deployment, the authors consider reference‐free approaches, namely approaches that do not require a good copy of the transmitted waveform to be available at the radar receiver. To this end, the authors first resort to a passive radar‐based processing scheme that only exploits the invariant a priori known initial portion of the physical layer protocol data unit, that is, the PHY Preamble, and its limitations in practical applications is investigated. Specifically, the authors show that, with this approach, an accurate time, phase, and frequency synchronization is essential and a possible strategy is investigated. As an alternative solution the authors consider a forward scatter radar‐based approach where only the amplitude modulation of the received signal is exploited to detect the presence of a moving target thus avoiding the need to know the transmitted signal. For the first time, the authors comparatively investigate advantages and drawbacks of the two reference‐free approaches and present practical strategies to handle the limitations observed. The results are reported for experimental tests with people and drones using WiFi transmissions in the 2.4 and 5 GHz band. [ABSTRACT FROM AUTHOR]

With the advent of constellations of SAR satellites, and the possibility of swarms of SAR UAV's, there is increased interest in multistatic SAR image formation. This may provide advantages including allowing three‐dimensional image formation free of clutter overlay; the coherent combination of bistatic SAR geometries for improved image resolution; and the collection of additional scattering information, including polarimetric. The polarimetric collection may provide useful target information, such as its orientation, polarisability, or number of interactions with the radar signal; distributed receivers would be more likely to capture any bright specular responses from targets in the scene, making target outlines distinct. Highlight results from multistatic polarimetric SAR experiments at the Cranfield University GBSAR laboratory are presented, illustrating the utility of the approach for fully sampled 3D SAR image formation, and for sparse aperture SAR 3D point‐cloud generation with a newly developed volumetric multistatic interferometry algorithm. [ABSTRACT FROM AUTHOR]

The marginal Fisher information (MFI) metric is used to design waveforms for the sake of informationally optimal adaptive‐on‐transmit radar operation. A framework for MFI waveform design is developed and the Polyphase‐Coded FM (PCFM) waveform model is utilised to produce a constant‐modulus, spectrally contained signal amenable to transmission with high‐power amplifiers. The efficacy of the MFI waveform design and minimum mean square error (MMSE) estimation is experimentally demonstrated and extended into an adaptive and dynamic sensing paradigm. The radar transmit waveform is optimised to maximise the Fisher information with respect to the range profile. Upon observing new information from radar echoes, the iterative MMSE (iMMSE) estimator then minimises the estimation error variance according to prior observations. Sequential information maximisation (via waveform design) and error minimisation (via iMMSE) tends towards the Cramér–Rao lower bound (CRLB) with additional measurements improving radar resolution and accuracy. These concepts maximise the information extracted by a radar operating in a congested spectrum where the available bandwidth is limited. [ABSTRACT FROM AUTHOR]

Urban surveillance of slow‐moving small targets such as drones and birds in low to medium airspace using radar presents significant challenges. Detecting, locating and identifying such low observable targets in strong clutter requires both innovation in radar hardware design and optimisation of processing algorithms. To this end, the University of Birmingham (UoB) has set‐up a testbed of two L‐band staring radars to support performance benchmarking using datasets of target and clutter from realistic urban environment. This testbed is also providing the vehicle to understand how novel radar architectures can enhance radar capabilities. Some of the challenges in installing the radar at the UoB campus are highligted. Detailed benchmarking results are provided from urban monostatic and bistatic field trials that form the basis for performance comparison against future hardware modification. The solution to the challenge of interfacing the radar to the external oscillators is described and stand‐alone bench tests with the candidate oscillators are reported. The testbed provides a valuable capability to undertake detailed analysis of performance of Quantum photonic‐enabled radar and allows for its comparison with conventional oscillator technology for surveillance of low observable targets in the presence of urban clutter. [ABSTRACT FROM AUTHOR]

In this article, a simple, yet effective, Bayesian scheme for tracks maintenance, promotion, and deletion in drone surveillance radar is presented. It enables the simultaneous tracking of the target body and micro‐Doppler components that originate from the motion of rotors (if any) onboard an unmanned air system. This not only delivers more accurate multi‐target tracking, but also substantially improves the radar automatic target classification capability (e.g. discriminating between drone and non‐drone targets). Challenging and diverse real staring radar datasets are used here to demonstrate the efficacy and benefits of the proposed track management approach. [ABSTRACT FROM AUTHOR]

The millimetre wave radar signatures of sea lions collected from three animals in the outdoor seal pool available at the Sea Mammal Research Unit in St Andrews in the Autumn of 2021 is reported. The objective is to study the radar amplitude and Doppler signatures of the animals when their full body or part thereof is above water, which is important for the application of autonomous marine navigation. The data was collected using 24 GHz (K‐band) and 77 GHz (W‐band) Frequency Modulated Continuous Wave radars with linear polarisation. It has been demonstrated that the sea lions were very clearly detected by the radars with Signal to Noise Ratio greater than 30 dB at a range of 40 m. The calculated modal radar cross section (RCS) of the sea lions in HH polarisation at 24 and 77 GHz vary from −48 to −26 dBsm and −48 to −28 dBsm respectively, corresponding to the different body parts and the amount of exposure to the radar beam. In VV polarisation, the modal RCS value range is from −49 to −26 dBsm and −49 to −22 dBsm respectively. The corresponding maximum RCS and the Cumulative Distribution Function results are also reported. [ABSTRACT FROM AUTHOR]

The paper focuses on the results of experimental research on the possibility of passive space object detection using a single radio telescope from the European Low‐Frequency Array (LOFAR) network of astronomical radio telescopes. Commercial digital television (DVB‐T) transmitters were used as illuminators of opportunity in this radar system. In the conducted experiments, one LOFAR radio telescope served both as a surveillance receiver and reference receiver in a passive radar system. The greater part of the LOFAR telescope array was used to observe a space object, while a small part of the array was directed towards the illuminator of opportunity to record the reference signal. One of the most important problems to overcome with utilising the LOFAR radio telescope in such a solution was the effective suppression of the direct‐path component in a surveillance signal coming from the illuminator of opportunity, which was relatively close to the LOFAR radio telescope. The results regarding the passive detection of the International Space Station, included in the paper, confirm the possibility of observing the space object flying in Low‐Earth Orbit using the LOFAR telescope or another receiving system with a similar antenna array. [ABSTRACT FROM AUTHOR]

This paper proposes a simple non‐adaptive approach for the suppression of direct signal and clutter contributions in a passive radar system based on orthogonal frequency division multiplexing transmissions, exploiting the properties of a reciprocal filter (RF). The use of a RF allows to circumvent the limits posed by the waveform ambiguity function, producing a data‐independent time‐invariant response to a stationary point‐like target echo at the output of the range compression stage. This feature enables simple clutter cancellation strategies, based on the subtraction of delayed portions of the surveillance signal, according to the conventional moving target indication methodologies. The proposed processing scheme consists of a range compression stage based on a RF, followed by a Single Canceller. Its performance is investigated against a simulated and an experimental data set, for the case of a digital video broadcasting‐terrestial‐based passive radar scenario, and it is compared to an alternative scheme based on a conventional matched filter and adaptive cancellation techniques. The proposed approach is shown to yield a perfect cancellation of the clutter returns from a stationary scene under ideal conditions. Moreover, it proves to be robust to real‐world conditions, despite its considerably limited computational load compared to adaptive clutter cancellation techniques. [ABSTRACT FROM AUTHOR]

This paper presents experimental verification results for a finite‐sized array of a simple non‐reciprocal metasurface elements. The metasurface elements consists of ferrite loaded metal patches. Experimental results demonstrate nonreciprocity, achieving isolation exceeding 20 dB at 6.4 GHz in a 2×2 array. Additionally, these findings are compared with simulation results for a 4×4 array to validate its practicality in a finite array setting. [ABSTRACT FROM AUTHOR]

Although radar sensors have been known for their robustness in challenging environmental conditions, they face difficulties in distinguishing between upper structure (US), such as road signs, traffic lights, overpasses, and pedestrian bridges, and forward vehicle (FV). This misclassification can lead to false alarms and unwanted braking events. In this paper, the authors propose a method distinguishing between US and FV at different distances based on change in radar cross section (RCS). The study collects real‐world RCS data from various objects identified as US and FV during road driving scenarios. [ABSTRACT FROM AUTHOR]

This article discusses the importance of radar systems and processing methods for space situational awareness (SSA). It highlights the vulnerability of the space environment due to the increasing number of satellites and space debris, as well as the growing threat of attacks. Radar sensors are particularly useful for SSA because they can detect small debris over long ranges and accurately track objects. The article presents various topics related to radar systems for SSA, including ground-based and space-based radar, multistatic radar networks, and cognitive radar and deep-learning methods. It concludes by providing an overview of the articles included in the special issue. [Extracted from the article]

Due to the controllable degrees of freedom of frequency diverse array in the distance dimension, the frequency diverse multiple‐input multiple‐output (FDA‐MIMO) radar can easily deal with mainlobe interference, which is often difficult to solve in traditional phased arrays. Nevertheless, the performance of FDA‐MIMO radar will suffer from degradation in the scenario where spectrum interferences and signal‐dependent interferences coexist. In order to solve this problem, a novel FDA‐MIMO radar framework via antenna switching is proposed. Based on this framework, an effective method for jointly optimising antenna selection and beamforming against spectrum interferences and signal‐dependent interferences is developed. The resulting optimization problem is nonconvex and NP‐hard owing to the integer constraints caused by antenna selection. By relaxing the integer constraints, the original problem can be transformed into a convex optimization form and an iterative reweighting strategy is used to force the obtained solution to satisfy the integer constraints. Simulation examples show that the proposed algorithm has better performance in output signal‐to‐interference‐plus‐noise ratio (SINR) and beampattern radiation performance than the existing competitive methods. [ABSTRACT FROM AUTHOR]

A great interest has been directed towards high frequency surface wave radar (HFSWR), because it is applied as long‐range early warning and real‐time measurements of sea surface condition tools in maritime surveillance. However, the unwanted reflection from the ionosphere (ionosphere clutter) damages the consistent detection performance of HFSWR. Target detection and environmental information extraction from radar echoes are primarily determined by the extent to which the medium contaminates the transmitted signals. Although the ionospheric signal corruption mechanisms are complex and various, a number of eliminating contamination approaches have been developed in the last 50 years. The purpose of this paper is providing an overview of the past and current clutter theoretical models and clutter suppression techniques for HFSWR system. The experimental results involved in the complex long‐term ionosphere observation and ionosphere echo analysis are first discussed. Special attention is paid to the correlation between the signal degradation sources and eliminating clutter techniques. Furthermore, the clutter suppression methods are classified based on the design idea. The specific clutter suppression methods as well as its operation principle are briefly introduced. Finally, several research trends and open issues are presented, with emphasis on the need for proactively controlling the radar design cost and generally agreed‐upon standards. [ABSTRACT FROM AUTHOR]

Active deception jamming recognition has gained significant attention as a crucial aspect of modern electronic warfare, and a large quantity of jamming recognition methods based on either artificial or deep learning methods have been proposed to date. In actual complex battlefields, the abundant deceptive jamming signals are extremely difficult to obtain and the deceptive jamming signals leveraged by the adversarial jammers are often significantly influenced by noise. To address these challenges a Siamese squeeze wavelet attention network (SSWAN) for radar active deception jamming recognition method is proposed. By constructing a wavelet attention module (WAM), the fine‐grained time‐frequency texture features of echo and jamming signals are preferably extracted even under prohibitively strong noise scenarios. Specifically, the Siamese structure is employed as the main backbone network to measure the similarity between jamming signals and make full use of the training samples; besides, the squeeze learning module is embedded to maintain lightweight and prevent overfitting. Experimental results demonstrate that at a jamming‐to‐noise ratio (JNR) of −8 dB, the proposed method achieves recognition accuracies above 96.3% for 6‐class and multi‐class combinational active deceptive jamming types. Overall, compared to mainstream deep networks, the proposed method exhibits superior advantages in lower JNR ratio and small samples of actual battlefield scenarios. [ABSTRACT FROM AUTHOR]

Interrupted sampling repeater jamming can produce numerous active false targets with high fidelity, which is a significant challenge for coherent pulse radar. This poses a threat to the effectiveness of anti‐jamming technology, such as time‐frequency analysis and frequency agility for conventional constant pulse repetition interval (PRI) radar, which will render the radar inoperable to detect and track the actual target. A random PRI and intra‐pulse frequency agile radar waveform with low interception properties is proposed, which can effectively restrict the acquisition of radar waveform parameters by the jammer, destroy the coherence of jamming signals, and capture the range‐dimensional centroid feature of the echo. Accordingly, a jamming identification method based on the scale constrained Gaussian mixture model and an entity radar target energe recovery algorithm based on the neighbourhood weighted maximum entropy are proposed for target detection. Theoretical analysis and simulation experiments demonstrate the effectiveness of the proposal. [ABSTRACT FROM AUTHOR]

The transmit waveform with ambiguity function specifications in the range‐Doppler plane has been of great interest in traditional single antenna radar systems in recent years. This paper considers the design of waveform sets with a desired slow‐time ambiguity function for multiple‐input multiple‐output radar. Specifically, it is desirable that the ambiguity function has low sidelobes where clutter occurs, as this reduces the impact of the clutter on target detection. Instead of unimodular waveforms, the authors consider amplitude modulation signals and introduce the bound constraint on signal amplitudes into the optimisation for the consideration of transmitter efficiency. The design problem is formulated as minimising the weighted integrated sidelobes level of the ambiguity function over the range‐Doppler plane under bound constraints. The resulting problem has a highly non‐linear objective function and is constrained by bound constraints and quadratic equality (constant energy) constraints, making it difficult to solve. The authors develop an efficient algorithm based on the sequential quadratic programming technique and non‐linear conjugate gradient method to solve it. Simulation results show that the proposed algorithm is superior to the existing methods. [ABSTRACT FROM AUTHOR]

Short‐wave source localisation has an important role in many fields. A two‐stage iterative algorithm is proposed based on the ionospheric virtual height (IVH) model using the direction of arrival (DOA) and time difference of arrival (TDOA) measurements, which is divided into DOA stage and TDOA stage. The optimisation problem of the TDOA stage can be obtained by using the result of DOA stage and TDOA measurements. During the solution process, the non‐convex quadratic equation constraints are converted to linear constraints, which can lead to the algorithm converges to the global optimal solution and the optimal solution can be analytically solved. The theoretical analysis demonstrates that the localisation accuracy of both stages can reach the corresponding constrained Cramér‐Rao lower bound (CCRLB). And the accuracy of TDOA stage achieves the CCRLB of hybrid localisation. Finally, the performance of the proposed method is demonstrated by simulations. [ABSTRACT FROM AUTHOR]

Various types of radar systems are increasingly being used to monitor aerial biodiversity. Each of these types has different detection capabilities and sensitivities to environmental conditions, which affect the quantity and quality of the measured objects of interest. Radar wind profilers have long been known to detect birds, but their use in ornithology has remained limited, largely because of biologists' unfamiliarity with these systems. Although the potential of radar wind profilers for quantitative bird monitoring has been illustrated with time series of raw data, a comparison with a similar radar system more established in biology is missing. Here, we compare nocturnal bird migration patterns observed by a radar wind profiler during October 2019 and April 2021 with those from a dedicated bird radar BirdScan MR1. The systems were located 50 km apart with an altitudinal difference of about 850 m. The nightly migration intensities measured with both systems were highly correlated in both spring and autumn (Pearson correlation coefficient ≈ 0.8, P < 0.001), but estimated traffic measured by the radar wind profiler was on average five times higher in spring and nine times higher in autumn. Low ratios of the migration traffic rates of the Birdscan MR1 to those of the radar wind profiler occurred primarily in clear conditions. In both radar systems, migration occurred at significantly higher altitudes in spring than in autumn. Discrepancies in absolute numbers between both systems are likely due to both system‐inherent and external environmental and topographical factors, but also different quantification approaches. These findings support the capacity of radar wind profilers for aerial biomonitoring, independent of environmental conditions, and open up further avenues for studying the impact of weather on bird migration at detailed temporal and altitudinal scales. [ABSTRACT FROM AUTHOR]

Measuring the radar backscatter characteristics of glacier ice at different frequencies and incidence angles is fundamental to predicting the glacier mapping performance of a sensor. However, such measurements at 94 GHz do not exist. To address this knowledge gap, we collected 94 GHz radar backscatter data from the surface of Rhônegletscher in Switzerland using the All‐Weather Volcano Topography Imaging Sensor (AVTIS2) real‐aperture Frequency Modulated Continuous Wave radar. We determine the mean normalized radar cross section σmean0 $\left({\sigma }_{\text{mean}}^{0}\right)$ to be −9.9 dB. The distribution closely follows a log‐normal distribution with a high goodness of fit (R2 = 0.99) which suggests that radar backscatter is diffuse and driven by surface roughness. Further, we quantified the uncertainty of AVTIS2 3D point clouds to be 1.30–3.72 m, which is smaller than other ground‐based glacier surface mapping radars. These results demonstrate that glacier surfaces are an efficient scattering target at 94 GHz, hence demonstrating the suitability of millimeter‐wave radar for glacier monitoring. Plain Language Summary: Radar sensors map glacier surfaces by transmitting a signal at a specific frequency and measuring its return strength when reflected back. This returned signal strength, called radar backscatter, is determined by the characteristics of the glacier surface and varies with radar frequency and sensor viewing angle. Millimeter‐wave radars operating at 94 GHz can acquire high resolution measurements of glaciers in most weather conditions. However, there are currently no measurements of radar backscatter from glacier surfaces at this frequency. We therefore acquired the first ever measurements of 94 GHz radar backscatter from glacier ice. The results are consistent with those expected from randomly rough surfaces, hence we conclude that the roughness of the glacier surface is the primary driver of 94 GHz radar backscatter. We also show that 3D glacier surface mapping at this frequency is more accurate than other ground‐based radars that are employed to map glacier geometries. The results overall indicate that 94 GHz radar is an effective tool for glacier monitoring and thus opens up new possibilities for studying glacier processes. Key Points: 94 GHz radar backscatter from alpine glacier ice has been characterized for the first timeSurface roughness is the primary factor in 94 GHz radar backscatter from glacier iceThe uncertainty of 3D glacier mapping using 94 GHz radar has been quantified [ABSTRACT FROM AUTHOR]

Stealth technology is used to enhance the survival of military equipment in the field of military surveillance, as it utilizes a combination of techniques to render itself undetectable by enemy radar systems. Radar absorbing materials (RAMs) are specialized materials used to reduce the reflection (or absorption) of radar signals to provide stealth capability, which is a core component of passive countermeasures in military applications. The properties of RAMs can be optimized by adjusting their composition, microstructure, and surface geometry. Carbon‐based materials present a promising approach for the fabrication of ultrathin, versatile, and high‐performance RAMs due to their large specific surface area, lightweight, excellent dielectric properties, high electrical conductivity, and stability under harsh conditions. This review begins with a brief history of stealth technology and an introduction to electromagnetic waves, radar systems, and radar absorbing materials. This is followed by a discussion of recent research progress in carbon‐based RAMs, including carbon blacks, carbon fibers, carbon nanotubes, graphite, graphene, and MXene, along with an in‐depth examination of the principles and strategies on electromagnetic attenuation characteristics. Hope this review will offer fresh perspectives on the design and fabrication of carbon‐based RAMs, thereby fostering a deeper fundamental understanding and promoting practical applications. [ABSTRACT FROM AUTHOR]

This paper outlines the construction of a new Multiplatform Precipitation Feature (MPF) framework designed to facilitate storm‐based analyses of data sets from multiple independent observing platforms for convective studies. The MPF approach is applied to ground‐ and space‐based precipitation observations and retrievals from the Global Precipitation Measurement mission Validation Network with space‐based lightning measurements from the Lightning Imaging Sensor on board the International Space Station to create a prototype MPF database. The data are synthesized in a thunderstorm‐like, feature‐based framework that enables broad statistical analyses of the storm‐based relationships between the microphysics, kinematics, and electrical properties of convection that can contribute insight into their morphological, regional, and seasonal variations. This manuscript details how MPFs are defined from precipitation data and how observations from multiple additional platforms are combined within the context of an MPF thunderstorm boundary and used to characterize the feature. A demonstration of the prototype database as a proof‐of‐concept showcases its flexibility to conduct both large‐scale storm‐based statistical studies as well as MPF‐facilitated thunderstorm case studies. Key Points: Demonstration database of individual thunderstorm‐like features combines independent data from multiple moving and fixed observing platformsPreliminary features include precipitation and lightning data collected from Global Precipitation Measurement and Lightning Imaging Sensor on board the International Space Station satellite instruments and ground‐based radarsDatabase enables cross‐platform, broad statistical thunderstorm‐centric analyses as well as detailed single‐storm case study reconstruction [ABSTRACT FROM AUTHOR]

To address the problems of traditional two‐level nested arrays (TTNA) with low degrees of freedom and high mutual coupling, a generalised two‐level nested MIMO radar array with high degrees of freedom and low mutual coupling for the direction of arrival (DOA) estimation is proposed. First, two coprime expansion factors are introduced in the TTNA to mitigate mutual coupling among array elements and enhance its degrees of freedom. Secondly, the range of consecutive lags and the total number of virtual array elements are derived in closed form under the structures of 'sum–difference co‐array'. Finally, utilising the atomic norm minimisation theory, the authors address the issue of discrete holes caused by different coprime expansion factors through filling in virtual arrays at discontinuities. Furthermore, a convex optimisation model to recover equivalent received signals from these filled virtual arrays is established, which is combined with a multiple signal classification algorithm for DOA estimation. Simulation results validate the rationality of the array structure and demonstrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Based on quantum illumination (QI) protocol, microwave quantum radar has been developed, which has attracted great interest from radar researchers. While, according to the QI protocol, the quantum advantage of QI radar is related to the power of generated signal, which may lead to a trade‐off between its quantum advantage and the maximum detection range. However, the maximum detection range is an important indicator to measure the performance of the radar. Therefore, it is necessary to quantitatively analyse the maximum detection range and quantum advantages of microwave QI radar, which will help us understand its performance from the perspective of radar applications and further evaluate the potential application scenarios and future research directions of it. The microwave quantum radar equation is developed, by the authors, based on the classical radar equation with the consideration of the quantum properties of extremely weak signals. Then, the maximum detection range of QI radar and classical two mode noise radar in different microwave bands is comprehensively compared and analysed. The possible applications of microwave QI radars in different bands are also discussed. In addition, the authors demonstrate that the symmetrical quantum limited amplification actually has no contribution to improving the detection range of a QI radar. [ABSTRACT FROM AUTHOR]

The usual procedure for extracting ocean surface information from data acquired from a high‐frequency surface wave radar transmitting from a floating platform is to first compensate for the motion of the antenna in the acquired motion‐contaminated Doppler spectrum and then extract the ocean wave parameters from the motion‐compensated result. The authors propose a new real‐time method to estimate the significant wave height directly from the antenna's received electric field in the time‐domain without requiring prior knowledge of the motion parameters or performing motion compensation. Based on the relation between the ocean surface displacement and the received electric field, this method calculates the significant wave height from the windowed variance of the upper envelope of the received electric field. A preliminary calibration is required, which can be carried out either by the deployment of a wave buoy or by analysing the data over a time period during which the sea state varies. The results from this simple proposed technique show that it may be used to estimate the significant wave height with a root‐mean‐square error (RMSE) between 10 and 14 cm over a wide range of significant wave height values. [ABSTRACT FROM AUTHOR]

The Gaussian noise model and estimators based on least squares (LS) are widely used in target localisation with distributed multiple‐input multiple‐output (MIMO) radar because of their computational efficiency. However, the accuracy of existing LS‐based target localisation algorithms deteriorates sharply in the presence of outliers in the measurements. Thus, a robust solution is developed based on the lp ${l}_{p}$‐norm minimisation criterion and iteratively reweighted least squares (IRLS) for locating a moving target with impulse noise using the angle of arrival (AOA), time delay (TD), and Doppler shift (DS) measurements. First, the AOA, TD, and DS measurement noise models are developed based on the α‐stable distribution. Then, the localisation problem is transformed into an lp ${l}_{p}$‐norm minimisation problem by linearising the AOA, TD, and DS measurement equations. Finally, the lp ${l}_{p}$‐norm minimisation problem is solved using an IRLS method to obtain the target position and estimate the velocity. Moreover, the optimum of the norm order (p) and the Cramér–Rao lower bound for the target position and velocity estimation are derived under α‐stable distributed measurement noise. The simulation results demonstrate that the developed algorithm offers higher accurascy and robustness than the existing ones in the presence of measurement outliers. [ABSTRACT FROM AUTHOR]

Traditional signal deinterleaving methods depend on preset parameters, use complicated steps and have limitations in handling complex electromagnetic environments where signals overlap in the space, time and frequency domains. This paper presents a novel approach using a deep segmentation network for radar signal deinterleaving. The pulse descriptor word data is transformed into a dot matrix image, which is then processed by a Cascade‐Recurrent Loop Network (CRLN) for segmentation. The CRLN consists of a Type Segmentation Network, an Amount Decision Network and a Recurrent Individual Segmentation Network. By recursively segmenting each target and determining the number of sources, the CRLN overcomes the challenges posed by the discontinuity and high overlap of individuals in the dot matrix image. Experimental results demonstrate the effectiveness of the proposed method, surpassing traditional deinterleaving methods, achieving high accuracy, low omission rate and effectively mitigates the increasing batch problem in complex scenarios. The experiments conducted on a randomly generated dataset yielded impressive results: over 90% accuracy when dealing with pulses from 15 radars of different types, over 96% accuracy when deinterleaving pulses from 4 radar individuals of a same type and over 90% accuracy when handling 5 radars of different types with 10% pulse loss. [ABSTRACT FROM AUTHOR]

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the "KuKa" dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in "stare" nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters. Plain Language Summary: Data collected using a surface‐based radar instrument on sea ice during the MOSAiC Arctic expedition were used to develop new techniques to estimate the depth of the overlying snow. We used different polarizations of the radiation to detect the depths of the upper and lower snow surfaces, and subtracted them to give snow depth. These depths agreed well with an independently collected snow depth data set. Estimates of snow depth using two different radar frequencies were less accurate, whilst using information of the shape of the returning pulse of radiation also showed a relationship with the independent snow depths, though not as strong as the polarization method. These results indicate that polarimetry (using a new satellite mission) and/or waveform shape (using existing missions) could be used to estimate snow depth on sea ice from airborne or satellite platforms. Key Points: Novel polarization‐based snow depth estimation techniques were developed using surface‐based Ku‐ and Ka‐band polarimetric radar altimeter dataThe dominant scattering surface was the air/snow and snow/ice interface in co‐ and cross‐polarized data, respectively, at both frequenciesRadar‐derived snow depths agreed with independent measurements, with r2 up to 0.77 and accuracy of 1 cm for best‐performing techniques [ABSTRACT FROM AUTHOR]

The coupling at the interface between tectonic plates is a key geophysical parameter to capture the frictional locking across plate boundaries and provides a means to estimate where tectonic strain is accumulating through time. Here, we use both interferometric radar (InSAR) and Global Navigation Satellite System (GNSS) data to investigate the plate coupling of the Hikurangi subduction zone beneath the North Island of New Zealand, where multiple slow slip cycles are superimposed on the long‐term loading. We estimate the plate coupling across the subduction zone over three multi‐year observational periods targeting different stages of the slow slip cycle. Our results highlight the importance of the observational time period when interpreting coupling maps, emphasizing the temporal variability of plate coupling. Leveraging multiple geodetic data sets, we demonstrate how InSAR provides powerful constraints on the spatial resolution of both plate coupling and slow fault slip, even in a region where a dense GNSS network exists. Plain Language Summary: Plate coupling as a concept describes to what degree the boundaries between tectonic plates are locked and building up stress. Such accumulated stress (over hundreds to thousands of years) will eventually be released in earthquakes, and therefore provides important information about the potential for future earthquakes. Our study uses satellite data to investigate how coupling between tectonic plates along the Hikurangi subduction zone (New Zealand's largest and most dangerous plate boundary fault) changes with time. We analyzed Interferometric Synthetic Aperture Radar and Global Navigation Satellite System data to map the areas where the plates are stuck together (coupled) and where they move past each other (uncoupled). We show that plate coupling varies significantly in space over 2, 4, and 10‐year time scales, highlighting the importance of carefully considering the observational time period when interpreting coupling maps. Key Points: Integration of high‐resolution displacement maps from radar imagery captures plate coupling at fine scalesEstimates of plate coupling depend strongly on the time period over which surface velocities are measuredTemporal variations in plate coupling highlight when and where slow slip dominates the slip budget [ABSTRACT FROM AUTHOR]

The jamming effect on radar is dominated by the design of the jamming waveform directly. Traditional jamming waveforms are generated using the template‐based method and are not environmentally resilient. Without the precise radar centre frequency support, using the direct guidance of the radar signal power spectrum density (PSD), a novel jamming waveform design method with an online unsupervised generative learning framework is proposed. The proposed framework consists of two modules: the waveform online generation module and the waveform unsupervised optimisation module. For the waveform online generation module, the well‐designed neural network (NN) is used to generate the jamming waveform. While for the waveform unsupervised iterative optimisation module, two loss functions are developed as two learning tasks to guide back‐propagation online. Given transmit power, the jamming PSD is designed adaptively to focus on the radar band and fit the radar PSD well, which improves the receiver in‐band jamming‐to‐signal ratio (JSR). It is worth noting that NN is end‐to‐end trained online without extensive prior training data. The framework can generate jamming waveforms within reasonable physical parameter ranges, and numerical experiments have shown that the designed jamming waveform performed better jamming effects on radar target detection in comparison with the traditional one. [ABSTRACT FROM AUTHOR]

In order to enhance the resource utilisation of the netted radar system, a receiving resource allocation (RRA) strategy for target localisation is developed in this article. Receiving resource refers to the receiving node and the processing approach adopted. The aim of the proposed RAA strategy is to select the receiver node and corresponding processing approach to minimise the resource cost while satisfying the desired target localisation accuracies. The resource cost includes the implementation cost of the processing approaches and the distance cost of data transmission. The Cramer‐Rao bound (CRB) incorporating the above controllable parameters is derived and used as the target localisation performance metric in the netted radar system. Whereafter, the RRA problem is modelled as a non‐convex constrained optimisation problem with the discrete variables. A scheme based on the modified depth‐first search is proposed to solve this problem. Simulation results demonstrate that the proposed scheme can effectively reduce resource consumption under the desired target localisation performance constraints. [ABSTRACT FROM AUTHOR]

Multiple‐Input‐Multiple‐Output (MIMO) radar has the characteristic of multiple antenna channels, bringing on a big data volume for signal processing. Therefore, the trade‐off between detection ability and computational efficiency is always considered. In this article, an optimisation system is proposed to enhance the real‐time performance of frequency division MIMO radar without compromising accuracy. For the scenario of low‐altitude small target detection, a signal processing acceleration method is proposed and a MIMO radar optimisation system based on graphics processing unit (GPU) architecture is built. The signal model of frequency division MIMO radar is first established, improving the classical signal processing flow efficiency from the perspective of reducing fast Fourier transform (FFT) times and windowing operation times. To achieve an advanced acceleration, the parallel architecture of ArrayFire‐library in GPU is then employed. Distinct minimum parallel units are extracted by analysing the principle of digital beamforming (DBF), pulse compression, moving target detection (MTD), and constant false‐alarm rate (CFAR) algorithms. And the corresponding parallel algorithms are designed to constitute a parallel acceleration system of frequency division MIMO. Simulation results indicate that the proposed method significantly improves the efficiency of MIMO system with a maximum acceleration ratio of 65 times, meeting the real‐time processing requirements. [ABSTRACT FROM AUTHOR]