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This article introduces a comprehensive Mode-S radar simulation, designed to evaluate interrogation and reply signal functionality in various uplink formats (UFs) and downlink formats (DFs). Mode-S radar, crucial in modern aviation, facilitates communication between aircraft and ground systems, thereby enhancing aircraft tracking, situational awareness, and air traffic safety. Our simulation focuses on optimizing interrogation and reply sequences in the 1030- and 1090-MHz channels, respectively. It provides a practical solution for testing complex air flight scenarios, which may be impractical or costly in real life, by replicating challenging conditions in a computerized environment. This approach aids in developing advanced radar signaling algorithms for air traffic control. The simulator’s user-friendly interface displays real-time aircraft data, demonstrating its realworld applicability. Its successful operation underscores its potential to advance Mode-S radar technology and its value for research in the field. [ABSTRACT FROM AUTHOR]

Radio sensing is a key envisioned feature for next-generation wireless systems, allowing the use of communication infrastructure and air interface components to also perceive the environment. Enabling efficient use of radio access network (RAN) resources to meet various communication and sensing use case requirements is a primary goal in such systems. This article bridges the gap among RAN architecture requirements, along with radio and signal processing techniques to realize the integration of radar sensing into next-generation cellular systems, e.g., the open RAN (O-RAN) framework. Motivated by the O-RAN disaggregated architecture, this article introduces a framework to analyze sensing processing from the perspective of the radio unit (RU)–distributed unit (DU) functional split and the fronthaul throughput requirements of interfaces between RUs and DUs. Accordingly, the feasibility of adopting sensing in current architectures while meeting fronthaul throughput limitations is demonstrated. Further, it is shown that receiver (Rx) processing techniques tailored for cellular system (e.g., to enable wideband sensing over disjoint bandwidths) can efficiently fit into the DU processing framework. Evaluation results revealing detection performance gains are also presented. [ABSTRACT FROM AUTHOR]

Fast settling phase locked loops (PLLs) play a pivotal role in many applications requiring rapid attainment of a stable frequency and phase. In modern communication standards, these PLLs are extensively utilized to guarantee precise compliance with dynamic resource allocation requirements. In processors, these PLLs manage dynamic voltage frequency scaling. Moreover, the fast-settling PLLs expedite the scanning of frequency spectra in sophisticated electronic radar set-ups, proving particularly advantageous for imaging and scanning radar applications. The rapid response exhibited by these PLLs is also harnessed in quantum technologies, catering to the urgent need for precise frequency adjustments to manipulate qubit states effectively. The strategies employed to attain fast-settling PLLs are primarily classified into five broad techniques in this article: enhanced phase frequency detection, hybrid multiple subsystems, VCO start-up, gear shift, and look-up table or finite state machine. This article explores the fundamental operational principles encompassing these techniques and presents optimal settling times for each method reported in the literature. Finally, the architectures utilizing these techniques will be evaluated based on their figure of merit (FoM), settling time, and tuning range. [ABSTRACT FROM AUTHOR]

Non-contact vital sign monitoring has been an important research topic recently due to the ability to monitor patients for an extended period especially during sleep without requiring uncomfortable attachments. Radar is a popular sensor for vital sign monitoring research. Various algorithms have been proposed for estimating respiration rate and heart rate from the radar data. But many algorithms rely on Fast Fourier Transform (FFT) to convert time domain signal to the frequency domain and estimate vital signs, despite FFT having limitation of frequency resolution being inverse of the time interval of data sample. However, there are other spectral estimation algorithms, which have not been much researched into the suitability of vital sign estimation using radar signals. In this paper, we compared eight different types of spectral estimation algorithms, including FFT, for respiration rate and heart rate estimation of stationary subjects in a controlled environment. The evaluation is based on extensive data consisting of different stationary subject positions. Considering the results, the eligibility of algorithms other than FFT for respiration rate and heart rate estimation is demonstrated. Using this work, researchers can get an overview on which algorithm is suitable for their work without the need to review individual algorithms separately. [ABSTRACT FROM AUTHOR]

High-resolution range profile (HRRP) is essential for high-throughput radar automatic target recognition. Due to the aspect sensitivity of HRRP, the recognition performance degrades when the training dataset misses some aspects. Inspired by disentangled representation learning (DRL), this letter proposes a type-aspect disentanglement network (TADN) to alleviate the above aspects missing problem. The proposed method disentangles HRRP into uncorrelated type and aspect representations, so that the type representation can be used to accurately recognize target without being affected by target aspects. Specifically, the proposed network acquires initial type and aspect representations by residual factorization. On this basis, mutual information (MI) minimization is used to constrain the independence of two representations from each other, and a reconstruction loss is employed to avoid information reduction during the decorrelation process. Additionally, target type and aspect labels are exploited to constrain their semantic meanings. Experimental results demonstrate that the proposed method achieves improved robustness in varying degrees of aspects missing cases. [ABSTRACT FROM AUTHOR]

Tropospheric refractivity, which is closely related to temperature, pressure, and relative humidity, is a valuable parameter for weather forecasting and climate analysis. It has already been demonstrated that refractivity estimates can be derived using the phase measurements corresponding to radar signals backscattered from stationary targets over any terrain orography, with high temporal resolution. However, the random distribution of stationary targets affects the spatial resolution provided by the computed refractivity estimates. It is of interest to obtain reliable radar-based refractivity maps to assist final users with data interpretation and analysis, so the use of a suitable geostatistical interpolation technique to obtain refractivity maps is studied in this letter. Refractivity estimates obtained from C-band radar data gathered during 2019 by the United Kingdom’s national weather service (Met Office) are used to evaluate the accuracy of the method by comparing the results to ground-based weather stations and the European Center for Medium-Range Weather Forecasts (ECMWF’s) ERA5 reanalysis dataset. [ABSTRACT FROM AUTHOR]

The traditional convolutional neural network (CNN)-based methods usually use the spatial information in the amplitude of complex synthetic aperture radar (SAR) images. Several studies have started to concentrate on merging the unique physical properties of SAR images, such as DSN-v1, extracting the backscattering characteristic from the frequency domain. Although DSN-v1 has obtained impressive classification ability, there is some room for improvement. In this letter, DSN-v2 is proposed to boost the classification ability of man-made and natural objects in SAR images. The improvement is reflected in two aspects. First, a multiscale subband feature extraction (MSFE) component is designed for natural objects. Since we observe their multiscale subband spectrum is significantly different, multiple encoders are used to extract effective features. Second, the additive angular margin (AAM) loss is introduced to distinguish man-made objects more clearly by manually adding a margin to the decision boundary. The experimental results on the Sentinel-1 (S1) dataset show that DSN-v2 achieves superior classification performance and model training speed compared with DSN-v1. [ABSTRACT FROM AUTHOR]

A multistatic inverse synthetic aperture radar (ISAR) system can observe a target from multiple observation angles. Compared with the monostatic ISAR system, the multistatic ISAR system can obtain more spatial sampling data, which provides the ability for high-resolution ISAR imaging. In some cases, the locations of radars are close. There are overlaps among observing angles, which brings little cross-range resolution improvement. However, such scenes are less considered indicates the literature. In the scene with overlaps, the image obtained by each radar may be similar due to the similar observation angles. In this letter, a novel multistatic ISAR imaging model is proposed by applying the similarity prior as a constraint. And an efficient image reconstruction algorithm is derived based on the orthogonality of observation matrix. Compared with existing CS-based methods, the proposed method can be directly applied on multistatic ISAR echoes without rearranging, which is more convenient in practical applications. Experiment results of simulated and measured data show that the proposed method achieves better performance especially under low signal-to-noise ratio (SNR) conditions. [ABSTRACT FROM AUTHOR]

To compensate for the repositioning error (RE) introduced by the radar position offset in discontinuous ground-based synthetic aperture radar (GBSAR) monitoring, a new mathematical framework for modeling the baseline error based on the Taylor expansion is developed in this letter. And then, a novel 3-D model called multiparameter nonlinear trigonometric model (MNTM) is proposed to accurately compensate for the RE. Furthermore, to improve the compensating efficiency, we further develop an efficient 2-D model called linear trigonometric model (LTM). Both simulation and field experiments verify the superiority and feasibility of the proposed methods, which measure the displacement with sub-millimeter accuracy. [ABSTRACT FROM AUTHOR]

The Doppler frequency of the echo signal exhibits time-varying and high-order characteristics due to the high-speed relative motion between the satellites in spaceborne inverse synthetic aperture radar (ISAR) imaging of space target, even during a short coherent processing interval (such as 1 s). Therefore, the selection of the optimal imaging time interval (OITI) is crucial to obtain a well-focused image. The existing methods do not consider the fact that the OITIs of scatterers on the target can also vary significantly. In this letter, an OITI selection method for spaceborne ISAR imaging of space targets based on the time–frequency analysis is proposed. First, the differences in optimal imaging instants (OIIs) of scatterers are analyzed. Then, based on the combination of the Margenau–Hill–spectrogram (MHS) and the S-method, the time–frequency curves can be obtained after extracting the time–frequency ridge and polynomial fitting. The OII of the entire target can be estimated after eliminating the differences in the OIIs by multiplying the constructed third-order phase terms with the signal. Finally, a more suitable OITI can be obtained because of the elimination of the differences in the OIIs. The effectiveness and advancement of the proposed method are verified by simulation and real data results. [ABSTRACT FROM AUTHOR]

High-fidelity deception jamming can seriously mislead synthetic aperture radar (SAR) image interpretation and target detection, which is difficult to identify or eliminate through traditional anti-jamming methods. Based on the range–Doppler algorithm (RDA), an anti-jamming method for SAR using joint waveform modulation and azimuth mismatched filtering is proposed in this letter. The signal model of SAR echo after pulse compression with deception jamming is derived from the radar detection and jamming characteristics. A multiobjective cost function is introduced to suppress the jamming level and keep the real target information, parameterized with the waveform initial phase and the azimuth mismatched filter coefficients, which is optimized using the second-order Taylor expansion approximation and the alternating direction multiplier method (ADMM). The performance of this proposed method is evaluated through the convergence analysis and anti-jamming experiments on the point target and the distributed target scenarios. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a powerful tool for all-weather, all-day observation, but it is susceptible to interference in the echo phase, particularly from the nonlinear phase caused by terrain relief. This can cause defocus and target position movement, which can impact the accuracy and recognition ability of SAR imaging. While current research focuses on using digital elevation models (DEMs) to correct the phase, obtaining high-precision DEM data requires significant measurement and processing work. To address this, we propose a new SAR filtering algorithm based on the terrain echo phase. By studying the phase of a SAR echo signal containing elevation information, we divide the nonlinear phase caused by terrain echo into two components: one that varies with azimuth and one that does not. We filter out the nonlinear phase component dependent on the azimuth angle from the echo phase and eliminate the nonlinear phase component invariant with the azimuth angle by filtering the specific pitch angle. The effectiveness of this filtering algorithm is verified through theoretical analysis and simulation. [ABSTRACT FROM AUTHOR]

Special attention has been devoted to joint waveforms based on radar signals to mitigate the tension of spectrum resources and simultaneously play a multifunctional role. Aiming at the shortcomings of conventional detection and jamming dual-functional systems, where wide beams are difficult to detect with high accuracy, while narrow beams are difficult to cover a wide range of jamming, this letter designs a multiple-input multiple-output (MIMO)-synthetic aperture radar (SAR) system with jamming capabilities. The characteristic of this signal is to use MIMO system multiple antennas to transmit frequency diversity wideband code division jamming signals to effectively generate wideband suppression jamming for conventional radars, including frequency-agile radars. At the same time, the wide beam characteristics of MIMO antennas can ensure the physical range of jamming. In addition, for the imaging part, digital beamforming (DBF) technology can be used to form the beam in a specific direction at the receiving end, resulting in high resolution. Simulation experiments confirm the system’s ability to achieve high-resolution imaging of specific target areas while maintaining robust jamming capabilities. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) using radar has been realized commonly using deep neural networks (DNNs). A change in radar operating frequency significantly changes the spectrogram characteristics compared to any other radar parameter. In this work, we consider three different approaches, viz., transfer learning, metric learning, and meta-learning (Reptile algorithm) for adapting the network developed for one radar operating frequency (source domain) to another operating frequency (target domain). Results and discussions on the performance of these algorithms on an openly available dataset are presented. [ABSTRACT FROM AUTHOR]

Vortex electromagnetic (EM) waves, which carry orbital angular momentum (OAM), can provide more information of the target in radar imaging and target detection. Pulsed radar is often used in current OAM radar design. However, compared with the pulsed radar, the frequency-modulated continuous-wave (FMCW) radar is small in size, light in weight, and low in power consumption, which can be applied into many practical scenarios. Therefore, this letter investigates into the OAM radar based on the FMCW system. It can be found that, apart from the inherent characteristics of small blind area and high measurement accuracy in FMCW system, the OAM-FMCW radar also owns an additional degree of freedom, which is independent of the frequency, finally leading to the result that the FMCW radar will not be affected by the range and velocity of the target when performing azimuth detection. Simulation results are given to validate the corresponding analysis of the proposed system. [ABSTRACT FROM AUTHOR]

In the dynamic game between jammers and radars, it is easily affected by the complex electromagnetic environment, resulting in the generation of asymmetric radar information. In this letter, a jamming effect evaluation method for asymmetric radar information is proposed from the perspective of a jammer. An online jamming effect evaluation model is constructed. An improved class-attribute contingency coefficient (ICACC) method is proposed. The feature attribute weight and inconsistency rate are used to constrain the division process of breakpoints, so that the discretization of radar data is more reasonable. At the same time, a basic belief assignment (BBA) correction method that combines probability similarity for asymmetric information is proposed. The output results of the support vector machine (SVM) are corrected to reduce the influence of incomplete radar sample data on the discriminant and evaluation results. Experiments show that the proposed method can achieve efficient jamming effect evaluation under asymmetric radar information conditions. [ABSTRACT FROM AUTHOR]

Accurate recognition of the types of mainlobe active deception jamming is essential for radar systems to take antijamming countermeasures. A bunch of deep-learning (DL)-based recognition methods that require large-scale datasets for training have shown promising results. However, capturing a sufficient number of diverse deception jamming samples is particularly intricate in actual dynamic and complex battlefields, the yielding limited or unbalanced datasets presents a significant challenge in training and generalizing DL models. This letter proposes a deep generative model, called the label-guided denoising diffusion probabilistic model (LG-DDPM), to address the issue of limited or class-imbalanced active deception jamming samples through data augmentation. By embedding label information into the diffusion process, the proposed model can generate and expand the active deception jamming samples specific to a predefined class even under low jamming-to-noise ratio (JNR) scenarios. The proposed method demonstrates superior performance in terms of both the fidelity and diversity of the generated jamming samples as well as the recognition accuracy of the DL recognizer when compared to state-of-the-art methods. Experimental results demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Effective oil-spill monitoring is critical for timely response to minimize the impact on the environment. In response to the difficulty in extracting suspected oil films from marine radar images, a semantic segmentation method was proposed. In the preprocessing of training samples in similar scene, a slicing solution was used to compensate for the small sample of original data. The U-Net semantic segmentation network was used to classify oil film into two categories: real and suspected. Existing mainstream marine radar oil-spill identification methods were compared. Experimental results demonstrate that the proposed method achieves more reliability in oil-spill semantic segmentation. It can provide real-time information for oil-spill emergency response and disaster assessment. [ABSTRACT FROM AUTHOR]

In this study, we introduce MobileViTX, an enhanced MobileViT architecture for human activity recognition (HAR) in impulse radio ultrawideband (IR-UWB) radar applications. MobileViT is a lightweight Vision Transformer mainly consisting of MobileViT blocks and MobileNetv2 blocks. Modifications to the MobileNetv2 block include adding a drop path and a squeeze-and-excitation (SE) module and altering activation functions to hard-sigmoid and hard-swish. Additionally, the self-attention in the MobileViT block is transformed to possess linear complexity. These adjustments aim to accelerate inference while preserving high accuracy. We experiment with a dataset from 20 individuals performing 20 distinct actions, using fivefold cross-validation to assess our model’s performance. Results show that MobileViTX outperforms the original MobileViT and other models in both recognition rate and efficiency. [ABSTRACT FROM AUTHOR]

Conventional monostatic SAR or DBS technology cannot realize forward-looking imaging due to Doppler symmetry ambiguity. Although the ambiguity can be resolved using multiple channels in azimuth, its azimuth resolution in the vicinity of flight path is usually poor due to the small angle variation. To solve the above problems, a hybrid real/synthetic aperture scheme for multichannel radar forward-looking imaging is proposed in this letter. In the scheme, the synthetic aperture imaging result with left/right ambiguity is obtained first using the information of platform motion. Then the real aperture superresolution imaging with regularized iterative adaptive approach (RIAA) is achieved using the instantaneous data of multiple channels. Finally, the two imaging results are fused to obtain the forward-looking superresolution image without left/right ambiguity. Simulation results are given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this letter, a constant false alarm rate (CFAR) detector is proposed for skywave over-the-horizon radar (OTHR) under Weibull distribution. First, an objective function, composed of a data fidelity term and regularization terms, is formulated in the Bayesian framework. The low rank prior of the distribution parameter matrix, smoothness of the parameters along the Doppler/range dimension, and sparsity of the target in the range-Doppler (RD) matrix are used to regularize the objective function. Then, the distribution parameters of all cells under test (CUTs) in the RD matrix are estimated by optimizing the objective function, and the abnormal CUTs with targets are identified and separated. Finally, target detection is performed by calculating the detection threshold through the threshold coefficient and estimated distribution parameters. Experiments on OTHR real data demonstrate that the proposed detector has a good CFAR property and significantly improved the probability of detection, especially in the case of multitarget and clutter edge. [ABSTRACT FROM AUTHOR]

In this letter, we designed a hemispherical refractive lens using ice for millimeter wave (MMW) imaging in the environment of −15 °C. Guided by geometrical optics theory, we calculated a suitable thickness of the lens to compensate for the phase of aperture field emitted by the antenna. Then, the 3 dB beamwidth was reduced from 17.6° to 2.1°. Meanwhile, the antenna gain is increased from 17 to 24 dB, which raises the maximum radar detection distance by 18%. Finally, we conducted experiments for MMW imaging to test the ice lens under different road conditions. Measuring results demonstrated that the image error rate has been significantly reduced and the peak signal-to-noise ratio (PSNR) has increased by more than 5 dB, which are consistent with simulated results. It should be noted that the ice lens can improve imaging quality and increase imaging resolution, making it widely applicable in the fields of Pole expedition and snow disaster rescue. [ABSTRACT FROM AUTHOR]

Along-track synthetic aperture radar interferometry (ATI) systems are used to detect a moving target and to estimate its radial velocity. A dual-channel (single-baseline) ATI system estimates the velocity ambiguously because there are many candidate velocities corresponding to the interferometric phase. To resolve the ambiguity of the estimates, a multichannel (multibaseline) ATI system is required. This letter proposes an efficient implementation technique of a multichannel ATI system by using a frequency-modulated continuous wave (FMCW) radar and an antenna arrangement method considering the inherent bi-static characteristic of FMCW radars. Also, a simple and robust radial velocity estimation algorithm suitable for the proposed system model is presented. The validity of the algorithm is confirmed with simulated ATI results. [ABSTRACT FROM AUTHOR]

Floods cause loss of lives and multibillion dollar damages every year. When these events strike, automated tools using remote sensing for quick mapping of the affected areas are critical for planning rescue activities and assessing impact. The most recent techniques to map floods are based on semantic segmentation and deep learning, which require large datasets and ground truth for training, which are difficult to get. To overcome this challenge, we propose an effective method for synthesizing patches of synthetic aperture radar (SAR) images containing open-land flooded areas. With bitemporal image acquisitions, we replace portions of land areas in the second acquisition with water pixels borrowed from permanent water bodies. Spatial patterns derived from elevation data help guide the process. With this approach, we build a large dataset with pre- and post-event Sentinel-1-based VH-polarized radar intensity images to train deep neural networks to map real-life floods. In a case study, we use an established U-Net architecture and show that a model version with less than 2% of the number of original parameters achieves almost identical flood detection accuracy. This allows for faster processing and is a clear advantage from an operational perspective. For comparison, we provide empirical segmentation results of four flood cases. F1 scores agree between 0.80 and 0.90 when compared with reference flood maps from the Copernicus service. This confirms that the approach is valuable for mapping areas affected by floods, e.g., during or immediately after catastrophic events, even in areas that were not included during model training. [ABSTRACT FROM AUTHOR]

In recent years, deep learning methods have significantly improved the recognition performance of high-resolution range profiles (HRRPs). However, the vulnerability of the deep network to attacks poses a serious threat to the security of radar target recognition systems. In this letter, an adversarial training algorithm based on contrastive learning is proposed that introduces the $N$ -pair loss function and balances the feature space to smooth the decision boundary and improve the robustness. The experimental analysis demonstrates that the proposed method achieves better defense performance than the traditional adversarial training algorithms. The work presented in this letter provides an important step toward improving the security and reliability of deep learning-based radar recognition systems. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a nondestructive detection technology. It uses high frequency electromagnetic energy to locate targets underground. In the direction of motion, GPR can produce Bscan with good performance. The lateral image is typically constructed using the results of many detections. By integrating beamforming with stepped frequency continuous wave (SFCW), we propose a method that increases the lateral detection performance. The performances of two beamforming methods in GPR are compared. We designed a $1\times4$ multistatic GPR and did two experiments. The findings demonstrate that the beamforming improves the lateral detection performance in comparison with single-static GPR. Compared with delay-and-sum methods, the minimum variance distortionless response (MVDR) method has a better performance in suppressing the sidelobe level and avoiding false targets. [ABSTRACT FROM AUTHOR]

The conventional range resolution improvement (RRI) technique combines two spectra of two received pulses with linear frequency modulation (LFM) in the range-frequency domain. However, its application to frequency-modulation continuous-wave (FMCW) radar is not straightforward. This study proposes an RRI method for two FMCW radars using cross-track interferometry (CTI), which are placed at different positions along the cross-track axis. Through the combination of the deramped signals of the two FMCW radars with the RRI, an improved high-resolution range profile (HRRP) was obtained. Furthermore, the simulation and experimental results validated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Wideband waveform design of multiple-input multiple-output (MIMO) radar with high resolution in complex electromagnetic environment is a challenging problem. To address the issue, this letter proposes a waveform optimization method based on multiple criteria, including beam pattern formation, frequency–space power distribution (FSPD) matching, and discrete ambiguity function (DAF) shaping. Considering the constant modulus constraint and Range-Doppler resolution requirement, the continuous phase code with a “thumbtack” ambiguity function (AF) is selected as the waveform form to be designed, and then, the waveform phase matrix becomes a variable to be optimized. Suppose a multitarget and bandwidth resource sharing scenario; under the premise of signal time-domain discreteness, both the space and bandwidth are divided into many discrete units, and a time–frequency–space joint optimization model with respect to the waveform phase matrix is established, so as to realize the beam pattern, FSPD, and DAF in compliance with expectations. Furthermore, aiming to solve the nonconvex optimization problem, a calculating approach based on conjugate gradient (CG) method is employed to achieve the closed-form solution of the multidimensional variable after simplifying the objective function. Compared with the traditional methods, the experiments verify that the proposed method can not only approximate the required multibeam shape and FSPD but also have good range-Doppler tolerance and sensitivity. [ABSTRACT FROM AUTHOR]

Multiview high-resolution range profiles (HRRPs) of aerial targets contain more target information than single-view one and will benefit accurate classification. However, feature information in HRRPs dynamically varies across different views, thus a dynamic classification framework is required to adjust the structure of the network along with the feature information variations and effectively make full use of those multiview features. To this end, we propose a dynamic-structure neural network (DSNN) with skip extraction and adaptive fusion blocks to adjust the network structure and adaptively fuse multiview features, enabling accurate aerial target HRRPs classification. In the skip extraction block, the skip gate automatically changes the block depth of each view to fit feature information variations, which ensures multiview HRRP features are dynamically exploited and extracted by the network. Then, in the adaptive fusion block, features from different views are weighted by the adaptive weight gate and effectively fused using associated attention, which further contributes to the classification. Besides, since the skip gate dynamically downsizes the extraction block for some views, the computational cost of DSNN is also reduced to some extent. Experimental results demonstrate that the proposed method has superior aerial target multiview HRRPs classification performance and computational efficiency over other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this letter, the clutter range dependence issue caused by crab angle is investigated in an airborne multichannel radar system. A new method is proposed to accomplish the crab angle estimation and the nonstationary error compensation. First, from the center-biased and spectrum-broadened characteristics of the clutter space-time spectrum in the case of nonneglected crab angle, a 1-D cost function based on space-time spectrum broadening degree is constructed to achieve the accurate crab angle estimation. Then, the clutter nonstationary errors can be effectively compensated in the post-Dopplerdomain,significantly improving the subsequent space-time adaptive processing (STAP) performance. Simulation results are presented to validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Sea surface salinity (SSS) is measured operationally with radiometers [e.g., soil moisture and ocean salinity (SMOS)] with a resolution from 25 to 40 km. In this contribution, we theoretically evaluate radar sensitivity to SSS, especially we focus on synthetic aperture radar (SAR) due to its intrinsic high-resolution imaging capabilities. This work focuses first on the variations in the dielectric properties of seawater with salinity at different SAR frequency bands (P-, L-, and C-bands) and then evaluate the consequences of both on the normalized radar cross section (NRCS) and copolar phase difference (CPD). The main challenge is to disentangle the sensitivity to dielectric properties from the surface roughness contribution in the SAR backscattered signals. In line with the literature, the results show that the NRCS sensitivity with salinity increases with decreasing operating frequency. In addition, we present new theoretical results on salinity sensitivity to CPD that have not been explored in the existing literature. [ABSTRACT FROM AUTHOR]

To address the issue that the existing multidomain fusion methods do not consider data correlation within mini-batch data, the first attempt is made in this letter to propose a method based on a sample intercorrelation learning multidomain fusion network (SIMFNet), which aims to accommodate multivariate domain data and further enhance the aquatic human activity recognition (AHAR) performance. To fully use the radar multidimensional information, the three-branch convolution neural network (CNN) feature extractor is first used to extract domain-specific features from the time–range map (TRM), time–Doppler map (TDM), and cadence velocity diagram (CVD). Then, the multidomain features are fused and fed into the graph construction layer (GCL) to generate instance graphs. Next, a graph aggregation layer (GAL) is applied to aggregate node information from various-hop neighborhood domains. Finally, node-level classification is used to achieve AHAR. The experimental results evaluated on the built AHAR dataset demonstrate that the proposed SIMFNet has better generalization performance than the state-of-the-art multidomain fusion methods. [ABSTRACT FROM AUTHOR]

This letter presents a novel method used to find the constant false alarm rate (CFAR) threshold after coherently compressing one or more dimensions of the radar image, generally the ones obtained from a phased array or multiple input multiple output (MIMO) diversity. The method provides a significantly large improvement in the numerical complexity of the processing chain at the cost of a slightly lower probability of detection for a fixed probability of false alarm. The method is particularly useful for real-time applications such as assisted and autonomous driving, where CFAR is applied to the range-Doppler map to generate a point cloud before angular estimation is done. The method may also be extended for Earth observation (EO) or surveillance applications where real-time processing is required. The proposed method is intended as a pre-processing step to the CFAR detection and is compatible with any CFAR algorithm that assumes Gaussian noise. The method implies finding a CFAR threshold calculated from a low-SNR non-coherent integrated radar signal and analytically mapping it to the high-SNR coherent integrated radar signal for improved detection. The proposed method is first presented theoretically and then evaluated in both simulated and experimental environments. [ABSTRACT FROM AUTHOR]

Sparse superresolution algorithms have been applied in scanning radar imaging to improve its azimuth resolution. However, the inverse matrix in each iteration is usually diagonal loading by the updating result, which leads to huge computational complexity for 2-D echo data. In this letter, a batch-processing superresolution framework is proposed to process the echo data in parallel. On the one hand, the optimization problem for sparse target recovery is modified as matrix form, which presents the batch-processing potential for 2-D echo data. On the other hand, the optimization problem is solved by the proposed alternating direction method of multipliers (ADMM)-based batch-processing framework, which can avoid high-dimensional matrix inversion along different range bins. Compared with traditional sparse superresolution methods, the proposed batch-processing framework is more suitable for 2-D echo data superresolution. [ABSTRACT FROM AUTHOR]

Micro-motion feature vectors of space targets are usually unevenly and multicluster distributed, which limits the performance of the traditional radar anomaly detection methods. To solve this problem, a novel detection method of radar space target abnormal motion method via local density peaks (LDPs) and micro-motion feature is proposed in this letter. First, two discriminative micro-motion features are extracted from the radar echoes to construct a 2-D feature space. Then the abnormal motion detector is derived by classifying the feature vectors into different clusters according to the LDPs and minimum spanning tree clustering (LDP-MST) and solving for the decision thresholds of each cluster with the LDPs, neighbors, and some preset false alarm rates. Electromagnetic simulation experiment results demonstrate that the detection rate of the proposed method is 2.49%, 5.26%, 9.63%, 15.37%, 27.99%, and 49.45% higher than six state-of-art methods, respectively, when the false alarm rate is 5%. [ABSTRACT FROM AUTHOR]

The National Weather Service has initiated an effort to upgrade the scan strategy of the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) to improve its hydrometeorological applications. The lowest scan elevation angle has been changed from 0.5° to 0° (or even lower in the future) for some WSR-88D stations. Using the KMUX WSR-88D radar deployed in mountainous terrain in Northern California as an example, this letter quantifies the impacts of lowering the minimum scan elevation angle of WSR-88D radar on surface rainfall mapping, with an emphasis on shallow orographic precipitation. In order to estimate surface rainfall using radar observations, polarimetric radar rainfall relations are established using local disdrometer data, which are then implemented with the KMUX radar observations at 0° and 0.5° scan elevation angles to derive rainfall estimates. Comparative evaluation of the radar-based rainfall estimates using rainfall measurements from surface rain gauges has demonstrated the superior performance of the lower scan elevation angle. When the distance from the radar is long (i.e., when the radar beam is likely within or above the melting layer), the improvement gained by the 0° scan relative to the 0.5° scan is 16.1% and 19.5% in terms of the normalized standard error (NSE) and the Pearson correlation coefficient (CORR), respectively. [ABSTRACT FROM AUTHOR]

To solve the problem of range-spread targets detection in non-Gaussian clutter for dual-polarimetric radar systems, a novel dual-polarimetric persymmetric detector based on the two-step generalized likelihood ratio test (GLRT) is proposed. First, the target signal is modeled as a multi-rank subspace signal, and the clutter is modeled as a compound Gaussian model with deterministic unknown texture. Then, by taking polarimetric and persymmetric characteristics of the clutter into account, the detection performance of the proposed detector is obviously improved. Finally, through the verification via both simulated data and measured data, it is shown that the proposed detector poses better detection performance in comparison with the existing nonpolarimetric detectors. Due to the complexity of the detector form, the analytical expression of false alarm probability cannot be derived. Therefore, we verified the constant false alarm ratio (CFAR) characteristic of the proposed detector through a simulation experiment. [ABSTRACT FROM AUTHOR]

Sea clutter modeling and parameter estimation/prediction are important basis of system design, performance assessment, and target detection of maritime radars. In addition to parameter estimation from measured data, parameter prediction from radar parameters, sea state, and the viewing geometry of the radar is an alternative. In this letter, a dual-polarimetric full-recorded sea clutter database measured by an island-based X-band experimental radar on the Yellow Sea of China is introduced. On this database, the model selection reveals that the compound-Gaussian model with inverse Gaussian (CGIG) texture is suited for HH high-resolution sea clutter data, the generalized Pareto (GP) intensity distributions are for HH moderate-resolution data, and the $K$ amplitude distributions are for high and moderate-resolution VV polarized data. Further, three five-parametric empirical formulas are constructed to predict the shape parameter of the CGIG, GP, and $K$ distributions of sea clutter from the area of spatial resolution cell, the grazing angle, the significant wave height (SWH), the average wave period, and the wave direction relative to the sight line of radar. The new empirical formulas attain more accurate prediction than existing empirical formula on the database. [ABSTRACT FROM AUTHOR]

The range-ambiguous clutter in space-based early warning radar is more severe compared with that in airborne radar due to the faster movement velocity. Meanwhile, the clutter angle-Doppler characteristics vary with range owing to Earth’s rotation. As a result, the mainbeam clutter returned from different ambiguous ranges occupies most of the Doppler spectrum, which leads to the degradation of the traditional space–time adaptive processing (STAP) performance in canceling clutter. Though frequency diverse array (FDA) based on multiple-input multiple-output (MIMO) can provide the ability to identify the range-ambiguous clutter, it cannot perform well for space-based early warning radar because of the finite available vertical elements. In this letter, a novel STAP method, which utilizes the element-pulse coding (EPC) and FDA technique to mitigate the range-ambiguous clutter, is proposed. First, EPC is used to prewhiten the most range-ambiguous clutter via coding and decoding in horizontal elements and coherent pulses. Then the interval in the vertical frequency of the residual range-ambiguous clutter is expanded by FDA, and thus the targets and clutter from the vertical mainlobe are easily extracted using a spatial filter with a few vertical elements. Finally, the extracted clutter can be effectively suppressed through the traditional STAP method. Simulation results are provided to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this letter, we address the problem of combating the effect of the nonlinearities of the transmit–receive chain in solid-state radars, which are typically generated by the power amplifier (PA) in transmission. This issue is of particular importance in meteorological radar applications, as they require to transmit pulse featuring very low autocorrelation sidelobes, which are significantly raised due to nonlinearities. In this work, we describe an adaptive predistortion design method based on an iterative approach in the spectral domain. The proposed method has been validated by means of hardware using a customized software radio and driving an amplifier into saturation. Two different ideal, highly performing pulse compression waveforms, expressly designed for weather radar applications, were sent to its input. The experimental results demonstrate the benefits of the proposed approach to design the digital predistortion module. [ABSTRACT FROM AUTHOR]

Noncooperative bistatic radars (NCBRs) do not transmit electromagnetic waves but use emitters of other host radars as illuminators for target detection. If the noncooperative bistatic radar and the host radar belong to the opposing camps, respectively, the noncooperative bistatic radar will be a great challenge to the host radar. To ensure the electromagnetic information security of host radars, a joint design of host radar waveforms and mismatched filters (MMFs) is proposed to reduce the detection performance of NCBRs. For NCBRs, we raise the autocorrelation function sidelobe levels of the transmitted waveform to deteriorate its detection performance. For host radars, we design an MMF to improve the target detection performance of such transmitted waveforms. The simulation results show that the effects of raising and suppressing the range sidelobes in the model are both achieved. The price is an additional SNR loss (about 2 dB). Two types of transmission waveforms are designed including mainlobe broadening (MLB) waveforms and false peaks (FPs) waveforms, by controlling the autocorrelation function elevated position parameter. The constant false alarm detection results show that the designed waveforms fulfill the requirements of noncooperative bistatic radar countermeasures. [ABSTRACT FROM AUTHOR]

Coast-transmit ship-receive (CTSR) bistatic high-frequency surface wave radar (HFSWR) can fully exploit the flexibility of moving platforms and the anti-interference advantages of bistatic radar. However, the platform motion can cause the spread of target echo in the frequency spectrum and reduction of amplitude, which is not conducive to detection. In this letter, a motion compensation method using a direct wave signal is proposed for CTSR bistatic HFSWR. First, the frequency shift characteristics of direct wave signal and target echo are analyzed by simulation. The simulation results show that the platform motion has a similar modulation form on both target echo and direct wave signal, and their difference is mainly caused by different azimuths. In addition, the direct wave signal is only affected by the platform motion, so it can be used to obtain the platform motion effect. Then, the proposed method uses a direct wave signal as the reference information to estimate and eliminate the phase modulation caused by the platform motion. At last, the effectiveness of the method is verified by the experiment based on simulated and measured attitude data. [ABSTRACT FROM AUTHOR]

The radon Fourier transform (RFT)-based coherent integration is an important target detection method. However, the computational cost of parameter searching and blind-speed sidelobe (BSSL) remain the main challenges in actual RFT applications. In this letter, we propose a Whale optimization algorithm-based RFT (WOA-RFT) to accelerate the parameter searching process and improve BSSL suppression performance. First, the discrete RFT signal model is derived; WOA-RFT is then proposed to speed up the RFT and suppress BSSL. The simulation results demonstrate that our proposed method has a better performance in terms of parameter estimation accuracy, BSSL suppression capability, and computational cost, compared to some existing methods. [ABSTRACT FROM AUTHOR]

Ground-penetrating radar (GPR) has been widely applied to the detection and delineation of buried targets in the subsurface. Compared with conventional single-channel GPR, polarimetric GPR has been proven to possess an improved ability to detect and characterize an elongated object in the subsurface. This letter proves that the scattering signals from a cylinder in two orthogonal polarization channels have a phase difference of about 90° when its diameter-to-wavelength ratio is about 0.05–0.33. Consequently, a polarization-difference imaging method, which shifts the VV component by −90° and subtracts it from the HH component, is proposed for the improved detection and imaging of a subsurface elongated object. Its effectiveness is verified by numerical, laboratory, and field tests on buried rebars and pipes. The signal-to-clutter ratios of the reconstructed GPR images can be improved by up to 4.5 dB by considering the phase difference between the dual-polarization components. [ABSTRACT FROM AUTHOR]

The multipath in very high-frequency (VHF) radar will reduce the direction-of-arrival (DOA) estimation accuracy for the low-angle target following. Since the target and multipath signals have strong coherence in the spatial domain, it is challenging to distinguish them accurately. Especially in some terrain complex regions, various scattering media cause the multipath echo to exhibit multichannel and nonuniform energy distribution features. Therefore, the single-multipath DOA estimation methods will mismatch the actual signal, resulting in inaccurate DOA estimation. The current letter presents a new DOA estimation approach using a multiple scattering center model in complex terrain to solve the mentioned issue. In the presented method, multiple multipath is analogous to a single one based on the multipath signal’s spatial coherence. The equivalent model represents the DOA estimation problem with a parametric optimization problem, and accurate estimation results can be attained using the Newton optimization approach. Compared with the conventional sparse reconstruction method, the presented method is not restricted by the restricted isometry property (RIP) and employs a robust parameter estimation method to solve the angle super-resolution problem under the low signal-to-noise ratio (SNR). The experimental results reflect that the presented model and approach can efficiently promote the DOA estimation precision and calculational performance compared to conventional DOA estimation approaches. [ABSTRACT FROM AUTHOR]

Sea clutter has been a long-standing issue in old and modern radars. Under this context, the K-distribution has emerged as a promising clutter model to accurately mimic sea signal variations in a large variety of radar systems. To guarantee an adequate radar performance in the presence of sea clutter, the family of constant false-alarm rate (CFAR) detectors has been commonly used. In particular, due to its adequate balance between performance and implementation, the cell-averaging CFAR (CA-CFAR) detector has been considered an attractive detection mechanism to enhance radar performance over various clutter environments. In this work, we assess radar performance considering a CA-CFAR detector operating over K-distributed sea clutter with fully correlated texture. More precisely, we derive novel closed-form expressions for the probability of detection ($P_{\text {D}}$) and the probability of false alarm ($P_{\text {FA}}$) that can be readily evaluated using any mathematical software. Monte-Carlo (MC) simulations corroborate our analytical findings. [ABSTRACT FROM AUTHOR]

This letter presents a radar waveform design for deceiving noncooperative bistatic radars (NCBRs). The principle of deceiving the noncooperative bistatic radar is proposed according to some imperfections in the engineering realization of NCBRs whose direct wave parameters are selected from the template library to reconstruct the reference signal. These imperfections are either unavoidable, such as phase asynchrony, or hard to find, such as time errors. For the first time, a radar waveform design method based on joint modulation of the initial phase and the pulse repetition interval (PRI) is proposed to achieve range migration–velocity deception for NCBRs. When our host radar illuminates our key target, the noncooperative bistatic radar will detect our key target. The proposed waveform will mislead the target detection results of the noncooperative bistatic radar in range–time (R–T) maps and range-Doppler (R-D) maps. It can not only be used to disguise a moving target as a stationary point target to avoid moving target detection (MTD) of NCBRs, but also modulate the target echo velocity into the opposite direction of the real motion to prevent our key moving targets from being tracked. Both the simulation data and the measured results show that the deception method proposed in this letter fulfills the requirements of NCBR countermeasures [ABSTRACT FROM AUTHOR]

Roadside millimeter-wave radar is one of the most important sensors in roadside perception systems, which have been widely used in intelligent traffic systems. It is important to find an effective calibration procedure for roadside radar to obtain the World Geodetic System-1984 (WGS-84) coordinates of detected targets. However, it is difficult to calibrate roadside millimeter-wave radar safely on public roads without road closures. To solve this problem, this article proposes a roadside millimeter-wave radar calibration method based on connected vehicle technology. First, all the trajectory points of vehicles detected by radar, including the connected vehicle, are collected, and the WGS-84 coordinates of the connected vehicle are obtained by vehicle-to-everything (V2X) communication with the roadside unit. Then, the trajectory points of vehicles are clustered with the density-based spatial clustering of applications with noise (DBSCAN) method, and the trajectory of the connected vehicle in the radar coordinate system is identified with the velocity-matching method automatically. The data pairs of the connected vehicle in the radar coordinate system and WGS-84 coordinate system are obtained with time synchronization. Finally, the calibration parameters are solved with five different types of optimization methods. We verify these methods on the beltway in Changsha. The results show that the improved pseudo inverse method (IPIM) and PIM achieve better performance than the extrinsic calibration method (ECM), rotate coordinate method (RCM), and improved RCM, and they can meet lane-level positioning requirements for V2X applications. This study provides an economical and effective way to solve the calibration problem for roadside millimeter-wave radar in an engineering project. [ABSTRACT FROM AUTHOR]

In this article, an asynchronous information fusion issue is investigated for a camera and a radar in an intelligent driving system. Local camera and radar estimators with missed detections are developed independently at synchronized state update time for target tracking. A Kuhn–Munkers algorithm is used to match local tracking results of camera and radar for fusion estimation of a same target. A fusion estimator is obtained by a matrix-weighted fusion algorithm with wide detection range and reliable fused estimates. Effectiveness of the proposed asynchronous fusion estimator is displayed by experimental results on road vehicle tracking. [ABSTRACT FROM AUTHOR]

Sea-surface object detection is critical for navigation safety of autonomous ships. Electro-optical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of these approaches. The article also proposes the direction of future research for sea-surface object detection based on EO sensors. [ABSTRACT FROM AUTHOR]