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A novel design of an embedded cardiorespiratory monitoring system for wheelchair users is proposed. The entire system is composed of a sensor node, a smartphone, and a cloud server. The sensor node contains two parts of an ultra-wideband pulse radar and the data processing module; the former is used to obtain continuous vital-sign signals, and the latter processes the sampled signals to estimate the heart rate and the respiration rate, implemented in an embedded system to achieve a fully integrated radar system. The smartphone functions as the data bridge between the sensor node and the cloud server, which is responsible for sending emergent messages. Experiment results show that the proposed system could work reliably in static and dynamic cases of wheelchairs.

The life detection radar plays an important role in earthquake rescue, but the vital signal acquired in practice is often submerged in noise. Improving the signal-to-noise ratio (SNR) of through-wa...

In recent years, human activity recognition using radar micro-Doppler signature has been the focus of many researches. Echo signal from body limbs is the origin of motion parameters estimation. To this end, decomposition of this composite signal into components corresponding to each limb is necessary and rather complex. In this paper, a novel method for body micro-Doppler signal decomposition is proposed using a deep convolutional neural network designated DecompNet which uses a context window on the spectral input stream to enhance the decomposition performance. This network is able to decompose 9 components of body limbs. The proposed network, is able to perform signal decomposition and denoising simultaneously, even in low SNR conditions. So, it outperforms the other similar methods both in terms of decomposition ability and robustness to observation noise. Simulation results show that DecompNet can outperform mathematical transform-based methods and also keep working in the SNRs as low as −15dB.

In automotive frequency-modulated continuous wave radar systems, targets with similar ranges and velocities might be detected as a single target because their target information overlaps on the range-velocity (RV) plane. When the information of multiple targets overlaps on the RV plane, target classification cannot be performed accurately. Therefore, we propose a method to decompose the overlapped target information on the RV plane for effective target classification. To separate each target’s radar signal, we use an orthogonal projection, in which the steering vector formed from the angle information of the desired target is projected onto the kernel space composed of other steering vectors. To verify the performance of the proposed method, a convolutional neural network-based target classification is performed on the decomposed radar signals. Compared to the angle-fast Fourier transform (FFT)-based signal decomposition, the orthogonal projection-based signal decomposition shows higher classification accuracy, which implies that the target information overlapped on the RV plane is effectively separated. When information of two targets is overlapped, the classification accuracy of the proposed method is 5.9%p higher than that of the conventional method. Moreover, when the information of three targets is overlapped, the accuracy is improved by 25.5%p.

The damage diagnosis of traditional hydraulic engineering structures is largely based on the modal analysis of contact approach, which makes it difficult to carry out a rapid diagnosis of the damage following an earthquake. In view of enhancing the efficiency of water conservancy engineering damage detection, this study was aimed at improving the traditional ant lion optimizer algorithm, combining it with wavelet transform to detect image denoising and effectively extract the features of the damage. Meanwhile, in terms of signal transmission and collection, the study adopted a geological radar as the carrier. In addition, the study verified the application prospects of nondestructive testing technology in the field of water conservancy engineering by evaluating the quality of a specific water conservancy project. Finally, the effectiveness of the algorithm was analyzed via experimental research. The results demonstrate that the proposed algorithm has clear advantages over traditional detection techniques.

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system., Comment: 8 pages, 11 figures, 5 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

The advantages of optical fiber as a transmission medium for microwave signals have led to an increasing interest in implementing RF photonic links in radar frontends. Many critical radar applications, such as those found in modern military systems, often require a large spur-free dynamic range (SFDR) that is beyond the reach of conventional intensity modulated-direct detection (IM-DD) fiber-optic links. For a solution, we propose a coherent amplitude-modulated link with optical down-conversion that can achieve high dynamic range by employing a receiver with balanced detection and digital linearization. The link is presented along with experimental validation. The measurements show notable dynamic range enhancements over prior arts in down-conversion RF photonic links.

As a new attempt to reject clutter in High Frequency Passive Bistatic Radar (HFPBR), the Cycle-Consistent Adversarial Network (CycleGAN)-based image domain clutter suppression algorithm works successfully by treating the clutter rejection problem as an image-to-image migration between the range-doppler spectrograms without and with clutter suppression. However, this algorithm also has some disadvantages such as too many calculation parameters, difficult model training and low clutter suppression rate. In this paper, a novel clutter suppression method based on an improved DenseNet CycleGAN (D-CycleGAN) is proposed. In this method, the ResNet block of the generator in the original CycleGAN network is replaced with the DenseNet block, to better extract clutter features. To better evaluate the clutter suppression performance of the deep learning network-based graph-relational method, a new index suitable for HF passive radar is introduced. Furthermore, the influence of the number of Dense blocks on the performance of the algorithm is also discussed. Finally, the performance of the proposed method is tested and verified on the measured data, compared with the traditional clutter suppression method and other popular image migration algorithms, such as Pix2pix. The results show that the D-CycleGAN-based method has better clutter suppression performance, stronger generalization ability, and better retained radar targets simultaneously.

In Europe, 20% of road crashes occur at intersections. In recent years, evolving communication technologies are making V2V and V2I faster and more reliable; with such advancements, these crashes, as well as their economic cost, can be partially reduced. In this work, we concentrate on straight path intersection collisions. Connectivity-based algorithms relying on 5G technology and smart sensors are presented and compared to a commercial radar AEB logic in order to evaluate performances and effectiveness in collision avoidance or mitigation. The aforementioned novel safety systems are tested in a blind intersection and low adherence scenario. The first algorithm proposed is obtained by incorporating connectivity information to the original control scheme, while the second algorithm proposed is a novel control logic fully capable of utilizing also adherence estimation provided by smart sensors. Test results show an improvement in terms of safety for both the architecture and high prospects for future developments.

Electronic beam steering is an essential feature of state-of-the-art radar systems. Conventional phased array (PA) radars with fixed carrier frequencies are well-known for electronically steering their beam with high directivity. However, the resulting beampattern is angle-dependent but range-independent. Recently, a new electronic beam steering concept, referred to as frequency diverse array (FDA) radar, has attracted increasing attention due to its unique range-angle dependent beampattern. More importantly, the FDA radar employs a small frequency increment across the array elements to achieve beam steering as a function of angle, range, and time. In this paper, we review the development of the FDA radar since its inception in 2006. Since the frequency offset attaches great importance in FDAs to determine the beampattern shape, initially much of the research and development were focused on designing the optimal frequency offsets for improved beampattern synthesis. Specifically, we analyze characteristics of the FDA beampattern synthesis using various frequency offsets. In addition to analyzing the FDA beampattern characteristics, this study also focuses on the neglected propagation process of the transmitted signals in the early FDA literature, and discuss the time-variant perspective of FDA beampatterns. Furthermore, FDA can also play a significant role in wireless communications, owing to its potential advantages over the conventional PAs. Therefore, we highlight its potential applications in wireless communication systems. Numerical simulations are implemented to illustrate the FDA beampattern characteristics with various frequency offset functions.

Radio frequency interference (RFI) can significantly pollute synthetic aperture radar (SAR) data and images, which is also harmful to SAR interferometry (InSAR) for retrieving elevational information. To address this issue, in recent years, a class of advanced RFI suppression methods has been proposed based on narrowband properties of RFI and sparsity assumptions of radar echoes or target reflectivity. However, for SAR echoes and the associated scene reflectivity, these assumptions are usually not feasible when the imaged scene is spatially extended. In view of these problems, this study proposes an InSAR-based RFI suppression method for the case of extended scenes. For this task, we combine the RFI-polluted SAR data with RFI-free interferometric data to form an interferometric SAR data pair. We show that such an InSAR data pair embeds an interferogram having the image amplitude multiplying by a complex exponential interferometric phase. We treat the interferogram as a kind of natural image and use discrete Fourier cosine transform (DCT) for its sparse representation. Then combining the DCT-domain sparsity with low-rank modeling of RFI, we retrieve the interferogram and reconstruct the SAR image via joint low-rank and sparse optimization. Numerical simulations show that the proposed method can effectively recover SAR images and interferometric phases from RFI-polluted SAR data.

Robust channel estimation in time-varying channels is used to guarantee the quality of communication services, especially for Vehicle-to-Everything (V2X) scenarios. To improve the channel estimation accuracy and reduce the pilot overhead, multi-input multi-output (MIMO) radar is deployed to assist millimeter wave (mmWave) channel estimation. In this paper, we propose a MIMO radar aided channel estimation scheme using deep learning (DL) for the uplink mmWave multiuser (MU)-MIMO communications. To allocate pilot resources reasonably, we design a transmission frame structure of joint radar module and communication module, which divides the estimation scheme into two stages, i.e., the arrival/departure (AoA/AoDs) estimation stage and the gain estimation stage. In view of the imperfections of array elements in practice, we propose an AoA/AoDs estimation algorithm based on subspace reconstruction in the AoA/AoDs estimation stage named two-step angle estimation (TSAE) algorithm. In the gain estimation stage, a DL based channel gain estimator is designed. An autoencoder combined with residual structure named residual denoising autoencoder (RDAE) is proposed to eliminate the noise on wireless signals, which is passed into the least square (LS) estimation module to obtain gains. Simulation results demonstrate that the MIMO radar aided and DL-based channel estimator provides the efficient estimation performance of the high-mobility mmWave channel with fewer training resources.

Simultaneous multitarget vital signs measurement has become a hot issue for noncontact vital signs perception. However, there is still challenge in the multitarget heartbeat measurement due to the weakness of heartbeat signal and interference from complex environment. In this article, a new multiple-input–multiple-output (MIMO) continuous-wave (CW) radar system equipped with 2-D digital beamforming (DBF) is presented to measure the respiration and heartbeat of multiple human subjects at unknown positions simultaneously. Through 2-D beam scanning of the whole scene, a 2-D radar image is generated. From the image, the chest motion of multiple targets is accurately located. Then, the vital signs of targets are obtained through forming individual beams focusing on the chests of targets. Moreover, the low intermediate frequency (low-IF) architecture is adopted to minimize the impact of flicker noise in low-frequency amplifier stages. The experimental results demonstrate that the proposed MIMO 2-D imaging radar system can locate chest areas of multiple targets, suppress the clutters, and make vital signs measurement, heartbeat measurement in particular, more robust compared with single-input–multiple-output (SIMO) radar system in complex environment.

This study reviews obstacle detection technologies in vegetation for autonomous vehicles or robots. Autonomous vehicles used in agriculture and as lawn mowers face many environmental obstacles that are difficult to recognize for the vehicle sensor. This review provides information on choosing appropriate sensors to detect obstacles through vegetation, based on experiments carried out in different agricultural fields. The experimental setup from the literature consists of sensors placed in front of obstacles, including a thermal camera; red, green, blue (RGB) camera; 360° camera; light detection and ranging (LiDAR); and radar. These sensors were used either in combination or single-handedly on agricultural vehicles to detect objects hidden inside the agricultural field. The thermal camera successfully detected hidden objects, such as barrels, human mannequins, and humans, as did LiDAR in one experiment. The RGB camera and stereo camera were less efficient at detecting hidden objects compared with protruding objects. Radar detects hidden objects easily but lacks resolution. Hyperspectral sensing systems can identify and classify objects, but they consume a lot of storage. To obtain clearer and more robust data of hidden objects in vegetation and extreme weather conditions, further experiments should be performed for various climatic conditions combining active and passive sensors.

The problem of strong sea clutter, e.g., sea spikes, may bring in low signal-to-clutter ratio (SCR) and cause great interference to radar marine target detection. However, the sea clutter suppression ability of current algorithms is limited with poor generalization under complex marine environment. In this letter, a novel sea clutter suppression generative adversarial network (SCS-GAN) is designed and employed for marine radar plan-position indicator (PPI) images detection. The SCS-GAN is based on residual networks and attention module, which includes residual attention generator (RAG) and sea clutter discriminator (SCD). In order to expand the data sets and improve generalization ability, clutter-free data set A, simulated sea clutter data set B (containing five types of sea clutter distributions), and actual sea clutter data set C are constructed by means of simulation and acquisition of real radar returns. At last, the parameter, i.e., clutter suppression ratio (CSR) is designed for evaluating the sea clutter suppression performances of the proposed method and other denoising and clutter suppression methods including CBM3D, denoising convolutional neural network (DnCNN), FFDNet, and Pix2pix. After testing with actual data, it is proved that the SCS-GAN has faster clutter removal speed, stronger generalization ability, and at the same time marine targets in images are remained completely.

In this paper, a dual-frequency continuous wave radar is proposed to achieve both localization and activity/ gesture recognition simultaneously. Specifically, features of different movements will be classified by the activity and gesture recognition network (AGRNet) which is a lightweight network based on MobileNet. The data that are recognized corresponding to walking will be used for moving target localization by comparing the phase difference in the Doppler domain between dual frequencies. In addition, a segmentation method is proposed to effectively segment continuous signals into individual time-periods corresponding to different motions by detecting the boundaries of signal changing. The experimental results show that the proposed method accomplishes the classification accuracy over 91% with 8 motion classes with a localization accuracy in the centimeter level.

In the actual scene of interferometric inverse synthetic aperture radar (InISAR) imaging, the noncooperative targets may make a nonuniform 3-D rotational motion (3-D-RM), which contributes not only to the time-variant Doppler modulation but also to the spatial-variant wave path difference (SVWPD). This, in turn, seriously degrades the 3-D geometry reconstruction accuracy of the targets. Furthermore, it is an enormous challenge to realize InISAR imaging from sparse frequency band and sparse aperture (SFB-SA) signals. This article seeks to address the problems of fine image registration and 2-D joint sparse reconstruction (2-D-JSR) for InISAR imaging with SFB-SA signals. With regard to the maneuvering targets with 3-D-RM, a novel SVWPD signal model is established. Moreover, a new algorithm, named joint wave path difference compensation (JWPDC) algorithm, is developed to perform fine image registration. It can not only combine multiple channels to achieve image registration but also jointly compensate for the non-SVWPD (NSVWPD) and SVWPD. A joint multichannel 2-D-JSR (JMC-2-D-JSR) ISAR imaging algorithm is also proposed according to the SFB-SA signal model to produce high-resolution ISAR images. Underpinned by the Bayesian compressive sensing (BCS) theory, the JMC-2-D-JSR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Through iterative processing of JMC-2-D-JSR and JWPDC, the high-quality 3-D InISAR images of maneuvering targets with 3-D-RM can be obtained. Extensive experimental results based on both simulated and real data corroborate the effectiveness of the proposed algorithm that outperforms other available InISAR imaging frameworks in 2-D imaging, 3-D imaging, and motion compensation.

Recent advances in radar systems have demonstrated the possibility of noncontact human vital signs detection. Most prior research has focused on vital sign detection with specific subjects’ orientations. So far, very few reported studies have been carried out on vital sign detection in any orientation, which is necessary to be applicable in various scenarios, e.g., assisting in the daily life of elderly persons living alone. To tackle this problem, this article presents a scheme that uses a distributed radar network, which transmits stepped frequency continuous-wave (SFCW) signals, then collecting the vital sign data returned from different directions of the subject and subsequently obtaining the respiratory and heartbeat rates from these collected data. A new respiratory and heartbeat model is proposed here for the theoretical analysis and simulation of vital sign detection for any orientation utilizing a distributed radar network. A modified independent component analysis (ICA) method noted as derivative independent component analysis (DICA) is proposed here to estimate the respiratory and heartbeat rates from multiple channels of the distributed radar network. Use of the ICA method was theoretically validated first to prove that the problem formulation satisfies the three conditions required for its implementation. Vital signs of a stationary subject were detected in simulation and measurement using multiple receiving antennas that are fan-shaped distributed around the subject from different directions. In this scenario, both the heartbeat and respiratory rates can be accurately estimated using the proposed DICA algorithm. Moreover, we also experimentally investigated the performance of the DICA method with different combinations of channels as input signals to the DICA algorithm. Both the simulated and experimental results have demonstrated that, by using a distributed radar network and the DICA method, a successful and accurate respiratory and heartbeat rate detection of a subject regardless of subject’s orientation can be achieved due to the independence of their non-Gaussian sources.

This article presents a fabrication method for a $C$ -band, low-power, frequency-modulated continuous wave (FMCW) radar front–end technology demonstrator that is fully embedded in a representative aerospace composite structure. This demonstrator uses a structural glass-fiber-reinforced polymer laminate as both the mechanical load-bearing element and the multilayer substrate for the integrated microwave circuitry. The system design and optimization have been described in detail. The electromagnetic simulation of the technology demonstrator was shown to be in excellent agreement with the measurement results. Finally, the FMCW radar front-end was tested as a complete system and shown to yield correct distance-to-target estimations. The demonstrated integration technique will lead to smart skins where electronic circuits and complete systems are embedded in load-bearing composite structures to yield truly multifunctional capabilities.

We report on the realization of a multichannel imaging radar that achieves uniform 2-D cross-range resolution by means of a linear array of a special form of leaky-wave antennas. The presented aperture concept enables a tradeoff between the available range resolution and a reduction in the number of channels required for a given angular resolution. The antenna front end is integrated within a multichannel radar based on stepped-carrier orthogonal frequency-division modulation, and the advantages and challenges specific to this combination are analyzed with respect to signal processing and a newly developed calibration routine. The system concept is fully implemented and verified in the form of a mobile demonstrator capable of soft real-time 3-D processing. By combining radio frequency (RF) components operating in the $W$ -band (85–105 GHz) with the presented aperture, a 3-D resolution of less than ${1.5}^\circ \times {1.5}^\circ \times $ 15 cm is demonstrated using only eight transmitters and eight receivers.

With the development of digital radiofrequency memory devices, the deception jamming has become a major jamming type in radar electronic counter countermeasures, especially the dense deception jamming. Recent years, some methods using signal features are proposed for specific deception jamming excision scene. It verifies the feasibility that uses signal features to discriminate deception jamming. However, discriminator training and simulation are indeed hard to achievable since no jamming samples can be obtained in advance. In this letter, we focus on the online dense deception jamming discrimination problem without the offline data training. Since deception jamming signals generated by jammers are definitely different from physical backscattering target echoes on signal features and dense jamming signals generated by one jammer has similar signal fingerprint features, an unsupervised clustering method with heuristic feature extraction is proposed. All discrimination processes are data driven and can be executed online without a priori information. To evaluate its effectiveness, the proposed method is applied to a group of experimental data collected from a trial C -band radar. Results corroborate the proposed method and indicate the direction of future research.

There has been significant research devoted to the development of distributed microwave wireless systems in recent years. The progression from large, single-platform wireless systems to collections of smaller, coordinated systems on separate platforms enables significant benefits for radar, remote sensing, communications, and other applications. The ultimate level of coordination between platforms is at the wavelength level, where separate platforms operate as a coherent distributed system. Wireless coherent distributed systems operate in essence as distributed phased arrays, and the signal gains that can be achieved scale proportionally to the number of transmitters squared multiplied by the number of receivers, providing potentially dramatic increases in wireless system capabilities enabled by increasing the number of nodes in the array. Coordinating the operations of nodes in a distributed array requires accurate control of the relative electrical states of the nodes. The basic challenge is the synchronization and stability of the relative phases of the signals transmitted or received. Generally, such control requires wireless frequency synchronization, phase calibration, and time alignment. For radar operations, phase control also requires high-accuracy knowledge of the relative positions of the nodes in the array to support beamforming. Various technologies have been developed in recent years to address the coordination challenges for closed-loop applications, such as distributed communications, and more recently, there has been growing interest in new technologies for open-loop applications, such as radar and remote sensing. This article presents an overview of distributed phased arrays, the principal challenges involved in their coordination, and recent research progress addressing these challenges.

This article proposes a method to detect vocal signals and the corresponding locations of multiple humans using a multiple-input and multiple-output (MIMO) frequency-modulated continuous-wave (FMCW) radar system. The signals of multiple humans are extracted with digital beamforming by the virtual array generated by MIMO radar processing. To remove body motion artifacts that distort the speech information detected by the radar, a body motion effect cancellation method is proposed. The technique allows recovering vocal signals from detected distorted signals by estimating phase variations due to body motions. A combination of the proposed vocal signal detection algorithm with a human localization one enables to detect speaking humans by means of the MIMO radar. Experiments verify that the proposed method can accurately locate and detect speaking humans based on their vocal vibration signals.

As the electromagnetic environment in battlefields is more and more complex, automatic modulation recognition for radar signals is becoming vital and challenging. Traditional methods are more likely to cause lower recognition accuracy with higher computational complexity in low signal-to-noise ratio (SNR). Feature redundancy especially for handcrafted features is one of the shortcomings of deep-learning-based methods. In this article, a novel end-to-end sequence-based network that consists of a shallow convolutional neural network, a bidirectional long short-term memory (Bi-LSTM) network strengthening with a self-attention mechanism, and a dense neural network is constructed to recognize eight kinds of intrapulse modulations of radar signals. The autocorrelation functions of received radar signals are first calculated as autocorrelation features. Then, these features are employed as inputs of the proposed network which owns significant sequence processing advantages and adaptive selection ability of features. Finally, the proposed network outputs prediction modulations directly. The simulation results verify the robustness and effectiveness of autocorrelation features. And the proposed network achieves about 61.25% accuracy at −20 dB and more than 95% accuracy at −10 dB. Compared with four state-of-the-art networks, the proposed network has better recognition performance especially at low SNRs with much lower computational complexity. Results on measured signals also demonstrate that the proposed network outperforms these four networks.

This work presents a cross correlation-based true-speed-over-ground estimation approach based on experiences from an earlier system. The radar system parameters as well as the antenna configuration were adapted to fit the requirements of the presented application. Also, challenges regarding the previously limited velocity range are addressed and an appropriate solution is proposed. The entire system was thoroughly tested in a train scenario over a wide range of velocities. Very promising measurement results are presented and compared to related approaches.

Wireless communications and sensing at terahertz (THz) band are increasingly investigated as promising short-range technologies because of the availability of high operational bandwidth at THz. In order to address the extremely high attenuation at THz, ultra-massive multiple-input multiple-output (MIMO) antenna systems have been proposed for THz communications to compensate propagation losses. However, the cost and power associated with fully digital beamformers of these huge antenna arrays are prohibitive. In this paper, we develop wideband hybrid beamformers based on both model-based and model-free techniques for a new group-of-subarrays (GoSA) ultra-massive MIMO structure in low-THz band. Further, driven by the recent developments to save the spectrum, we propose beamformers for a joint ultra-massive MIMO radar-communications system, wherein the base station serves multi-antenna user equipment (RX), and tracks radar targets by generating multiple beams toward both RX and the targets. We formulate the GoSA beamformer design as an optimization problem to provide a trade-off between the unconstrained communications beamformers and the desired radar beamformers. To mitigate the beam split effect at THz band arising from frequency-independent analog beamformers, we propose a phase correction technique to align the beams of multiple subcarriers toward a single physical direction. To further decrease the ultra-massive MIMO computational complexity and enhance robustness, we also implement deep learning solutions to the proposed model-based hybrid beamformers. Numerical experiments demonstrate that both techniques outperform the conventional approaches in terms of spectral efficiency and radar beampatterns, as well as exhibiting less hardware cost and computation time., 17pages14figures. Accepted in IEEE Journal of Selected Topics in Signal Processing

Radar-based human activity recognition (HAR) finds various applications like assisted living and driver behavior monitoring. As radar data are heavily environment-dependent, it is becoming increasingly important to develop a transfer learning mechanism that enables a radar-based HAR system with desirable cross-environment adaptation feasibility. This paper concerns the issue of how radar-based HAR system can adapt to a new environment without source data. To this end, we devote to using the source hypothesis transfer learning architecture to build such an environment adaptation mechanism towards cross-environment radar-based HAR. In doing this, it is a challenging task to develop a reliable self-supervised labeling strategy for generating pseudo labels associated with the unlabeled target data, which is crucial to facilitate the learning of a target-specific feature extractor being responsible for environment adaptation. This paper presents the neighbor-aggregating-based labeling method and incorporates it with the existing clustering-based labeling method to perform the self-supervised labeling task. The logic behind our approach is that the above two labeling methods are complementary to each other in terms of making use of both local and global structures of adaptation data to supervise the labeling task. The coordination of both labeling methods is motivated to be implemented in the weighted combination form, which contributes to improving the reliability of generated labels. Experimental results on a public HAR dataset based on the frequency modulated continuous wave (FMCW) radar demonstrate the effectiveness of our approach.

We propose a drone classification method for polarimetric radar, based on convolutional neural network (CNN) and image processing methods. The proposed method improves drone classification accuracy when the micro-Doppler signature is very weak by the aspect angle. To utilize received polarimetric signal, we propose a novel image structure for three-channel image classification CNN. To reduce the size of data from four different polarization while securing high classification accuracy, an image processing method and structure are introduced. The data set is prepared for a three type of drone, with a polarimetric Ku-band frequency modulated continuous wave (FMCW) radar system. Proposed method is tested and verified in an anechoic chamber environment for fast evaluation. A famous CNN structure, GoogLeNet, is used to evaluate the effect of the proposed radar preprocessing. The result showed that the proposed method improved the accuracy from 89.9% to 99.8%, compared with single polarized micro-Doppler image. We compared the result from the proposed method with conventional polarimetric radar image structure and achieved similar accuracy while having half of full polarimetric data.

Multiple-input multiple-output (MIMO) has been used in wireless communications to increase data rates via multiplexing or improve performance via diversity. Additionally, in radar systems, MIMO promises improved parameter identifiability and target resolution. Frequency diverse (FD)-MIMO radar can effectively distinguish targets that are closely spaced in the same angle cell by exploiting its range-angle-dependent transmit beamform. Therefore, in this paper, we first propose an integrated waveform design by embedding weighted, phase-modulated communication signals in the FD-MIMO radar waveform. Then, we derive the Cramer-Rao lower bound (CRLB) of the location estimation and analyze the communication performance achieved when the communication receiver extracts information from the integrated signals. Next, transmit beamforming is optimized to achieve the best tradeoff between the radar and communication performances of the system. Due to the nonconvexity of the optimization problem, we apply sequential parametric convex approximation (SPCA) and semidefinite relaxation (SDR) methods to solve the problem. The simulation results reveal the effects of some parameters, such as the frequency interval, symbol period, and communication symbol sequence, on radar and communication performance and demonstrate the tradeoff between radar and communication obtained by optimizing the transmit beamforming vector.

Recently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). This paper proposes an automatic recognition method for different LPI radar signal modulations. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR.

Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.

The NASA Orbital Debris Program Office (ODPO) develops, maintains, and updates orbital debris environmental models, such as the NASA Orbital Debris Engineering Model (ORDEM), to support satellite designers and operators by estimating the risk from orbital debris impacts on their vehicles in orbit. Updates to ORDEM utilize the most recent validated datasets from radar, optical, and in situ sources to provide estimates of the debris flux as a function of size, material density, impact speed, and direction along a mission orbit. On-going efforts within the NASA ODPO to update the next version of ORDEM include a new parameter that highly affects the damage risk – shape. Shape can be binned by material density and size to better understand the damage assessments on spacecraft. The in situ and laboratory research activities at the NASA ODPO are focused on cataloging and characterizing fragments from a laboratory hypervelocity-impact test using a high-fidelity, mock-up satellite, DebriSat, in controlled and instrumented laboratory conditions. DebriSat is representative of present-day, low Earth orbit satellites, having been constructed with modern spacecraft materials and techniques. The DebriSat fragment ensemble provides a variety of shapes, bulk densities, and dimensions. Fragments down to 2 mm in size are being characterized by their physical and derived properties. A subset of fragments is being analyzed further in NASA’s Optical Measurement Center (OMC) using broadband, bidirectional reflectance measurements to provide insight into the optical-based NASA Size Estimation Model. Additionally, pre-impact spectral measurements on a subset of DebriSat materials were acquired for baseline material characterization. This paper provides an overview of DebriSat, the status of the project, and ongoing fragment characterization efforts within the OMC.

To make a better discrimination between target and false alarm, amplitude information (AI) of radar signal has been incorporated into many target tracking algorithms, such as probabilistic data association, multiple hypothesis tracking and probability density hypothesis (PHD). In this paper, we present the cubature integration based Gaussian mixture implementation of the PHD filter with AI, namely the GMPHD-AI filter. Since the utilization of multiple sensors is more effective than using only one sensor, we also extend the GMPHD-AI filter to the multi-sensor application where the iterated-corrector scheme is exploited for multi-sensor information fusion. The effectiveness of the presented method is demonstrated by a multiple targets tracking scenario.

This paper discusses the utilisation of deep reinforcement learning algorithms to obtain optimal paths for an aircraft to avoid or minimise radar detection and tracking. A modular approach is adopted to formulate the problem, including the aircraft kinematics model, aircraft radar cross-section model and radar tracking model. A virtual environment is designed for single and multiple radar cases to obtain optimal paths. The optimal trajectories are generated through deep reinforcement learning in this study. Specifically, three algorithms, namely deep deterministic policy gradient, trust region policy optimisation and proximal policy optimisation, are used to find optimal paths for five test cases. The comparison is carried out based on six performance indicators. The investigation proves the importance of these reinforcement learning algorithms in optimal path planning. The results indicate that the proximal policy optimisation approach performed better for optimal paths in general.

This chapter examines the technical capabilities of orbital earth observation sensors and considers associated geomorphic user requirements between 1957 and 2000. Early photography from space provides much promise, which is followed by successive dedicated satellite missions. These produce data at ever-greater spatial and spectral resolution, as well as frequency, starting with Landsat 1 in 1972. Users especially in the arid and polar community are drawing on this new technology. However, the wider uptake of data derived from passive systems such as Landsat and other such sensors by geomorphologists is sporadic due to the mismatch between data requirements and systems specifications. Limitations to most geomorphologists would have included data cost, given that even Landsat data were not always freely available, as well as data volume and processing capabilities, which favoured governmental scientists. Active radar data on some levels fulfilled the geomorphic requirements such as retrieval of form and texture, as well as height. Unfortunately, the processing of such image data required significant technical capabilities and was not easy to interpret, given the numerous variables associated with the backscatter of the microwave signal. Early Earth observation missions were deployed at a time when the full extent of global change, driven by anthropogenic activity, had not been fully comprehended. However, older legacy data are now of profound value, since they provide a comparative baseline against which change can be quantified. The study period also saw the gradual transfer of military GPS capabilities to the civilian sector, which facilitated field activities and geometric correction of imagery.

Multichannel tracking radars with phased antenna arrays are widely used to track air targets. The use of a phased array in combination with digital computing technology allows to control the radar radiation pattern and track several targets in the time distribution mode. Air target tracking in a multichannel radar is provided by subsystems for measuring range, radial velocity and angular coordinates, in most cases, without adaptation to the external influence characteristics. When tracking supermaneuverable air targets, such as 5th and so-called 4++ generation fighters, there is a decrease in the accuracy and stability of tracking relative to the area without maneuver. If the tracking system algorithms are tuned to a low intensity of maneuvering or its absence, a significant increase in the error of tracking the aircraft in the maneuvering section will lead to disruption of auto tracking due to a significant dynamic component of the error. The stability of auto-tracking of maneuvering targets by subsystems of range, radial velocity, and angular coordinates with fixed parameters for the case when the setting of the parameters of the tracking system algorithms coincide with the characteristics of the external influence is analyzes in the paper. The influence of the observation model parameters, the stochastic model of the target movement with exponentially correlated values of the target acceleration, and the measurement period of the target coordinates on the potential tracking accuracy by radio technical tracking systems of the multichannel radar is investigated. To assess the stability of auto-tracking, it is proposed to use the equivalent aperture size of the discriminating characteristic. The influence of the parameters of the target movement stochastic model, the observation model, and the measurement period of the target coordinates on the stability of auto-tracking in terms of range, radial velocity, and angular coordinates is estimated. It is shown that the "weak link" is the radial velocity tracking system. As a result of the research carried out, it becomes possible to further assess the feasibility of adapting the auto-tracking systems to the target maneuvering characteristics and to develop recommendations for choosing the measurement period of the target coordinates.

The synthetic aperture radar (SAR) image of ground moving target is easily defocused given the unknown relative motions between radar and target. In addition, the limitation of pulse repetition frequency for SAR easily induces the azimuth ambiguity (including Doppler center blur and spectrum ambiguity) of target signals, which causes the difficulty of moving target imaging. In this paper, a new efficient method for imaging and motion parameter estimation of ground moving targets with azimuth ambiguity is presented. Firstly, the time reversal process-2D scaled Fourier transform (TRP-2DSCFT) is developed to eliminate the effects of cross-track velocity and estimate the along-track velocity of target, simultaneously. Secondly, an operation based on discrete polynomial-phase transform and phase compensation function is proposed to estimate the cross-track velocity of target. Finally, a matched filter function based on estimated parameters is constructed to focus the moving targets. The well-focused result can be obtained by the presented method without the residual compensation errors. Moreover, the proposed method is computationally efficient given that the exhaustive searching steps are avoided. Additionally, the azimuth ambiguity can be effectively removed without the ambiguity number searching and pre-processing based on the prior information. The cross-terms for multiple target processing are analyzed. The effectiveness of the method is verified by both spaceborne and airborne real data results.

Early recognition of gestures gives a better user experience during human–computer interaction in a near real-time way. However, the inadequate information accumulated in the early stages of the gesture will make the machine ambiguous and respond wrongly. Therefore, the challenge of early recognition lies in choosing the right time to trigger gesture prediction and using limited information to achieve reliable accuracy. In this article, an early gesture recognition method is proposed to achieve almost the same accuracy as a complete gesture sequence. For this purpose, we design a new architecture to modify the current feature sequence according to the existing information and introduce a new loss function to maximize the probability of correct gesture label as early as possible. At the same time, gesture recognition is triggered when the maximum probability of model output is greater than a set threshold. The publicly available data set used for evaluation comes from Google’s Project Soli sensor. Experimental results show that the proposed method for early recognition can achieve a gesture recognition rate of 88.85% with an average time saving of 69.47%. Compared with the results of all sequence recognition, the recognition rate drops by only 1.7%. In addition, the proposed method based on the data set constructed by terahertz radar also shows similar early recognition performance and can be applied to other radar sensors.

With the advances in radar technology, there is an increasing interest in radar-based applications for automated surveillance, tracking, and recognition. Radars capture the Doppler-induced frequency shifts, which can be utilized to recognize the small motions by vibrating or rotating parts of the target. This paper introduces a fast and compact deep Gabor neural network for human motion classification using backscattered signals from a continuous-wave Doppler radar. The joint time-frequency representation produced by the S-method is employed to depict the micro-Doppler signature of a walking person. We introduce a new Gabor layer as a generic feature extractor for designing compact neural architectures. Each Gabor layer consists of several steerable Gabor filters that can be trained to extract the salient micro-Doppler features and improve computation efficiency. For human motion classification from a distance, the Deep Gabor Network (DGN) is trained in an end-to-end manner to process the local patches in the time-frequency representation of the radar signal. A Bayesian optimization technique is employed to select the optimal network hyperparameters. The experimental results on a real radar dataset show that the proposed method achieves competitive classification rates compared to several existing approaches while having a significantly smaller model size and shorter prediction time.

Emotion recognition has a significant impact on people’s health and life quality. Recent studies have shown that emotion recognition can be achieved by analyzing audio-visual emotion channels and physiological signals. However, the main challenges are: 1. the audio-visual techniques can be easily fooled by fake facial expressions; 2. the contact physiological signal monitoring introduces additional stress and is usually unsuitable for long-term monitoring; 3. The non-contact physiological signal monitoring methods are easily affected by complex environmental conditions. In this paper, a non-contact dual-modality emotion recognition system is proposed. First, the respiratory and heartbeat signals are measured by radar and camera simultaneously. Then, a hybrid signal optimization approach is proposed to remove the influence of body motion and light conditions on the physiological signal. It includes a light-intensity-based scheme selecting proper heartbeat signal for different light conditions, and an optical-flow-based algorithm pruning signals with significant radial body motion. Finally, the features extracted from the optimized physiological signals are fused to train an emotion recognition system. As shown in the experimental results, the proposed system could achieve high classification accuracy of 89.6% for 10-fold cross-validation at sample level, and 71.0 % for cross-validation at subject level. The respiratory and heartbeat signals by non-contact approaches are demonstrated to be reliable in emotion recognition.

For studying the flow of glaciers and their response to climate change it is important to detect glacier surges. Here, we compute within Google Earth Engine the normalized differences between winter maxima of Sentinel-1 C-band radar backscatter image stacks over subsequent years. We arrive at a global map of annual backscatter changes, which are for glaciers in most cases related to changed crevassing associated with surge-type activity. For our demonstration period 2018–2019 we detected 69 surging glaciers, with many of them not classified so far as surge type. Comparison with glacier surface velocities shows that we reliably find known surge activities. Our method can support operational monitoring of glacier surges, and some other special events such as large rock and snow avalanches.

Achieving general 3D motion estimation for all-visibility has been a key challenge in robotics, especially in extreme environments. The widely adopted camera and LiDAR-based motion estimation critically deteriorate under fog or smoke. In this letter, we devised a unique sensor system for 3D velocity estimation by compositing two orthogonal radar sensors. The proposed configuration allowed securing returns from the static objects on the ground. This work aimed at a realistic sensor deployment in a harsh environment by casing the bare sensor rig into a plastic box. As will be shown, the proposed velocity-based ego-motion estimation presented reliable performance over existing point matching-based methods, which degraded when measurement attenuates due to the casing. Furthermore, we introduce a novel radar instant velocity factor for pose-graph simultaneous localization and mapping (SLAM) framework and solve for 3D ego-motion in the integration with IMU. The validation reveals that the proposed method can be applied to estimate general 3D motion in both indoor and outdoor, targetting various visibility and the structureness in the environment.

Radar specific emitter identification (SEI) is the process of uniquely identifying an individual emitter from the same class of radars by their individual properties that arise from hardware imperfections. However, it is challenging or perhaps impossible to generate globally unique emitter identifiers by using SEI techniques alone, due to the increasing number of radars and the subtle differences in their signal properties. We therefore introduce a multitier radar emitter identification concept that includes radar function identification, model identification, and SEI. The combination of function identifiers, model identifiers, and specific emitter identifiers could generate globally unique radar emitter identifiers. In this article, we propose to use the radio frequency (RF) features extracted from time domain transient signals for radar model identification. The RF features include the duration, maximum derivative, skewness, kurtosis, mean, variance, fractal dimension, Shannon entropy, and polynomial coefficients of the normalized energy trajectory of a transient signal, as well as the area under the trajectory curve. We propose three RF fingerprints for radar model identification, each consisting of a predetermined subset of the features. The performance of the RF fingerprints was evaluated by using five classification algorithms with two radar datasets. Our results show attractive performance with respect to hetero-model radar identification. In particular, for the hetero-model radar dataset, the pair of the all-features (AF) fingerprint and gradient boosting algorithm achieved 91.9, 90.25, and 91.1% classification accuracy for three, four, and five emitters, respectively. On this basis, we conclude that the proposed AF fingerprint could be applied directly to radar model identification by training the gradient boosting algorithm using a dataset with many radars per model.

To broaden the bandwidth of high-gain substrate-integrated waveguide (SIW) cavity-backed slot antennas, an empty SIW (ESIW) cavity-backed antenna with multiple high-order hybrid modes is proposed. Due to the ESIW configuration, the proposed antenna is independent on substrates and a high efficiency more than 90% in the operational band can be realized even the typical low-cost high-loss FR4 substrate is used. The proposed antenna has a wide measured bandwidth of 40.8% and the maximum measured realized gain can reach 11.1 dBi. The proposed broadband high-gain antenna can be used in radar and communication systems.

Accurate beam tracking in high-mobility vehicular networks has become a key issue for reliable communication link establishment. Recent work on integrated sensing and communication has shown the superiority of the sensing-assisted beam tracking. By estimating the angular parameters of vehicles based on the radar echoes, the roadside unit (RSU) can perform a so-called predictive beamforming to pair the beams with high accuracy as well as low latency. To further reduce the estimation error due to the nonlinear measurements, in this letter, we develop a deep learning-based approach to exploit the dependency between the angles and the measurements, and to obtain the angle estimates. Simulation results show that our proposed approach can improve the estimation performance and maintain reliable communications in high-mobility vehicular networks.

As small satellites are finding increased applicability in a number of space missions, the community is actively exploring the feasibility of missions employing active instruments on small spacecraft. In particular, synthetic aperture radars are of interest as they prove to be versatile tools for Earth Observation purposes. This paper proposes a trade space exploration model to identify designs for space-borne satellite synthetic aperture radars that address the desired instrument requirements. Two trade space analysis approaches are proposed, based on a multivariate analysis with two evaluation metrics and using parallel coordinate plots. The paper demonstrates the utility of the trade space model by considering two case studies. The first study looks at radar instruments on a broad range of small satellite platforms. This study narrows down a trade space of 1265 feasible radar designs to less than 44 Pareto optimal designs at different center frequencies (C- and X-band), elicits conditions for L-band SARs feasibility, and discusses the feasibility bounds and technical constraints on associated instrument and spacecraft requirements. The second study considers radars for 3U CubeSat platforms. The study identifies 44 Pareto optimal designs within 12,928 feasible designs and sheds light on the operational constraints required for the development of such innovative miniaturized radars.

Respiration and heartbeats rates are important physiological assessment indicators that provide valid prior-knowledge for the diagnosis of numerous diseases. However, most of the current research focuses on the vital signs measurement of single target, and multi-target vital signs detection has not received much attention. In this paper, we use frequency-modulated continuous wave (FMCW) radar to measure the vital signs signals of multi-target. First, we apply the three-dimensional fast Fourier transform (3D-FFT) method to separate multiple targets and get their distance and azimuth information. Subsequently, the linear constrained minimum variance-based adaptive beamforming (LCMV-ADBF) technique is proposed to form a spatially distributed beams on the targets of interest directions. Finally, a compressive sensing based on orthogonal matching pursuit (CS-OMP) method and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) method are present to extract the respiratory and heartbeat signals. We perform tests in a real experimental environment and compare the proposed method with reference devices. The results show that the degrees of agreement for respiratory and heartbeat are 89% and 87%, respectively, for two human targets. The level of agreement for respiratory and heartbeat is 87% and 85%, respectively, for three human targets, proving the effectiveness of the proposed method.