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This study investigates the different aspects of multimedia computing in Video Synthetic Aperture Radar (Video-SAR) as a new mode of radar imaging for real-time remote sensing and surveillance. This research also considers new suggestions in the systematic design, research taxonomy, and future trends of radar data processing. Despite the conventional modes of SAR imaging, Video-SAR can generate video sequences to obtain online monitoring and green surveillance throughout the day and night (regardless of light sources) in all weathers. First, an introduction to Video-SAR is presented. Then, some specific properties of this imaging mode are reviewed. Particularly, this research covers one of the most important aspects of the Video-SAR systems, namely, the systematic design requirements, and also some new types of visual distortions which are different from the distortions, artifacts and noises observed in the conventional imaging radar. In addition, some topics on the general features and high-performance computing of Video-SAR towards radar communications through Unmanned Aerial Vehicle (UAV) platforms, Internet of Multimedia Things (IoMT), Video-SAR data processing issues, and real-world applications are investigated.

This paper presents a high-gain slotted ridge waveguide array antenna (SRWAA) with inductive diaphragms, which can realize a wide-sector beam with a low sidelobe simultaneously. The proposed antenna can cover a wide detection range and avoid interference from other directions. The expected excitation distribution for the antenna array is extracted through a beamforming method. To reduce the influence of the dispersion phenomenon on signal quality, inductive diaphragms are inserted into the sidewall of the ridge waveguide, which is fully analyzed from the point of the equivalent circuit. A cut-off-mode power divider is utilized, which can control the power ratio flexibly. A SRWAA working at 24.125 GHz, including a six-way feeding network, and a 6×24 slot array with the size of 330 mm × 66.8 mm is designed and fabricated. The measured sidelobe level (SLL) and half-power beamwidth (HPBW) in the elevation plane are -19.6 dB and 54.41°, with the counterparts in the azimuth plane -29.8 dB and 3.15°, respectively. The measured peak gain is 22.3 dBi at 24.125 GHz. The measured results are in good agreement with the simulated ones. This work has the potential to be applied in air detection, anti-unmanned aerial vehicles (UAVs), meteorological radar, and imaging radar.

The higher bandwidth inverse synthetic aperture radar (ISAR) can obtain the higher resolution radar images, which can provide more target information and help improve radar target detection and recognition. It is essential to study how to achieve a precise high-resolution (HR) ISAR image utilizing limited measurement echoes. The existing neural-network-based ISAR imaging methods extract features only from limited measurement echoes, and the common features in HR ISAR images are not utilized sufficiently, which limits the imaging performance improvement. Moreover, in their loss functions, there are no explicit constraints on the correct recovery of strong scattering points, which are important in reflecting the target characteristics. In this letter, we propose a conditional probabilistic generative model to achieve the HR ISAR imaging. By optimizing the well-designed Kullback-Leibler (KL) divergence between conditional prior and approximate posterior probability distribution in the loss function, the common features contained in training HR radar images can be learned, and a suitable prior probability distribution for the latent variable can be obtained. To accurately recover the positions and relative amplitudes of strong scattering points, we blend a similarity measurement that is sensitive to the large values' locations in a vector with the adversarial loss. Both visual and numerical results of extensive experiments prove that the proposed model can obtain enhanced effectiveness and efficiency compared with some counterparts.

Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.

Nonline-of-sight (NLOS) radar imaging is a novel technique that can inverse the scattering characteristics of targets in the NLOS area, which has been one of the hot pots of radar imaging field. However, the existing NLOS radar mainly focuses on 1-D or 2-D imaging, which inevitably suffers from the geometric loss of real 3-D scenes, and its applications are restricted in the urban environment. In this article, we propose an NLOS radar 3-D imaging model and method for looking around corner (LAC) situation by multi-input-multioutput (MIMO) millimeter-wave (mmW) array antennas. In this scheme, first, the model of NLOS radar 3-D imaging with mmW MIMO antennas is established and the multipath scattering of targets with this model is analyzed. Then, the theoretical resolution of LAC 3-D imaging is derived and discussed. Second, exploiting the three bounces of LAC and extraction of linear structure, an effective imaging algorithm with mirror projection theory and Radon transform, dubbed as mirror symmetry backprojection (MSBP), is proposed for 3-D image focusing. Moreover, to suppress the uncertainties of phase caused by both LAC and system error, the minimum entropy principle is introduced to MSBP. Finally, an NLOS 3-D imaging system with 77-GHz mmW MIMO radio frequency module and 2-D rails is developed. Different types of targets, such as metal balls and ornaments, are tested in LAC. The results demonstrate that our NLOS technique can not only provide a high-quality 3-D focusing of the hidden targets but also extract positions of targets without prior knowledge of the NLOS area.

Low imaging spatial resolution hinders through-the-wall radar imaging (TWRI) from reconstructing complete human postures. This letter mainly discusses a convolutional neural network (CNN)-based human posture reconstruction method for TWRI. The training process follows a supervision-prediction learning pipeline inspired by the cross-modal learning technique. Specifically, optical images and TWRI signals are collected simultaneously using a self-develop radar containing an optical camera. Then, the optical images are processed with a computer-vision-based supervision network to generate ground-truth human skeletons. Next, the same type of skeleton is predicted from corresponding TWRI signals using a prediction network. After training, the model shows complete predictions in wall-occlusive scenarios solely using TWRI signals. Experiments show comparable quantitative results with the state-of-the-art vision-based methods in nonwall-occlusive scenarios and accurate qualitative results with wall occlusion.

Polysomnography (PSG) is the gold-standard for sleep apnea and hypopnea syndrome (SAHS) diagnosis. Because the PSG system is not suitable for long-term continuous use owing to the high cost and discomfort caused by attached multi-channel sensors, alternative methods using a non-contact sensor have been investigated. However, the existing methods have limitations in that the radar-person distance is fixed, and the detected apnea hypopnea (AH) event cannot be provided in real-time. In this paper, therefore, we propose a novel approach for real-time AH event detection with impulse-radio ultra-wideband (IR-UWB) radar using a deep learning model. 36 PSG recordings and simultaneously measured IR-UWB radar data were used in the experiments. After the clutter was removed, IR-UWB radar images were segmented by sliding a 20-s window at 1-s shift, and categorized into two classes: AH and N. A hybrid model combining the convolutional neural networks and long short-term memory networks was trained with the data, which consisted of class-balanced segments. Time sequenced classified outputs were then fed to an event detector to identify valid AH events. Therefore, the proposed method showed a Cohen’s kappa coefficient of 0.728, sensitivity of 0.781, specificity of 0.956, and an accuracy of 0.930. According to the apnea-hypopnea index (AHI) estimation analysis, the Pearson’s correlation coefficient between the estimated AHI and reference AHI was 0.97. In addition, the average accuracy and kappa of SAHS diagnosis was 0.98 and 0.96, respectively, for AHI cutoffs of ≥ 5, 15, and 30 events/h. The proposed method achieved the state-of-the-art performance for classifying SAHS severity without any hand-engineered feature regardless of the user’s location. Our approach can be utilized for a cost-effective and reliable SAHS monitoring system in a home environment.

This letter deals with an imaging strategy able to manage effectively data collected on multiple lines by means of a Mini-Unmanned Aerial Vehicle (M-UAV) radar system operating in Sounder mode. The strategy, named multilines imaging approach (MIA), allows an effective 3-D pseudo-representation of the investigated volume. At the first step, MIA faces the problem of reconstructing 2-D domains (slices) by exploiting data collected on one or more lines. Then, MIA interpolates the 2-D reconstructions to provide the 3-D representation. The imaging of each slice is formulated as a linear inverse scattering problem, which is solved by means of the truncated singular value decomposition (TSVD) regularization scheme. The MIA effectiveness is assessed by processing real data collected at Archeological Park of Paestum and Velia, Paestum, Italy.

A microwave photonic multiple-input and multiple-output (MIMO) radar is proposed and demonstrated to implement high-resolution imaging. In the proposed system, multiple orthogonal linearly frequency modulated (LFM) signals are generated by heterodyning between two optical frequency combs, which enables a MIMO transmitting array with a simple and reconfigurable structure. The receiving array uses photonic frequency mixing to implement multiple channel separation and de-chirp processing simultaneously. This microwave photonic MIMO radar can have a large operation bandwidth and a large equivalent aperture, which helps to achieve high-resolution imaging in both range and azimuth directions. In the experiment, a microwave photonic 48 MIMO radar is established with a 2-GHz bandwidth in each channel. Based on this MIMO radar, high-resolution back-projection (BP) imaging with a theoretical range resolution of 7.5 cm and azimuth resolution of 1.85 is demonstrated. The experimental results can verify the feasibility of the proposed MIMO radar, which is a good solution to high-resolution radar imaging by combining microwave photonic and MIMO technologies.

Millimeter-wave (MMW) radar sensors are considered key components of autonomous vehicles. Because of the performance degeneration of cameras and lidars under inclement weather conditions, robust autonomy must rely on radar sensors to perform target detection and classification. Unlike basic target classifier methods in literature that make use of target velocity, the proposed approach is far more comprehensive and can be applied to targets with zero-Doppler. Depending on the radar type and target range, this paper presents four target classification models based on four different types of radar data: statistical RCS, distributed (time-domain) RCS, range-azimuth angle radar images and 3D radar images. The classification models are implemented by machine learning approaches artificial neural network (ANN) and convolutional neural network (CNN) with a comprehensive simulated dataset. Good classification accuracies are demonstrated, and the proposed model is validated with measured data. Different radar target classification approaches are compared, which clearly reveals the trade-off between classification performance and system complexity. The proposed radar target classification methods can be applied effectively to both static and dynamic targets, at near or far ranges, using traditional or imaging radars, resulting in improved safety for autonomous vehicles in a wide variety of complex environments.

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

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.

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.

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 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.

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.

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.

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.

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.

Microwave photonic (MWP) radar has the advantages of generating and processing wide bandwidth microwave signals, reconfigurability, high immunity to electromagnetic interference compared to microwave electronic radar. It has the potential to be used in applications such as intelligent autonomous and cyber-physical systems. Recent advances in microwave photonic technology led to the generation, fast processing, and control of broadband signals. Because of the advancements in photonic technologies, next-generation microwave photonic radar is becoming more prominent. This article reviews the most recent advancements and future directions in MWP radars. This review article overviews the different components of microwave photonic radar, different design challenges, and issues pertaining to it. We present a comparative study of different MWP radars on different applications. It also discusses possible future research directions of MWP radar.

Ghost targets caused by inter-reflections are by design unavoidable in radar measurements, and it is challenging to distinguish these artifact detections from real ones. In this letter, we propose a novel approach to detect radar ghost targets by using LiDAR data as a reference. For this, we adopt a multimodal transformer network to learn interactions between points. We employ self-attention to exchange information between radar points, and local crossmodal attention to infuse information from surrounding LiDAR points. The key idea is that a ghost target should have higher semantic affinity with the reflected real target than the other ones. Extensive experiments on nuScenes [1] show that our method outperforms the baseline method on radar ghost target detection by a large margin.

Automatic detection of a falling person based on noncontact sensing is a challenging problem with applications in smart homes for elderly care. In this article, we propose a radar-based fall detection technique based on time-frequency analysis and convolutional neural networks. The time-frequency analysis is performed by applying the short-time Fourier transform to each radar return signal. The resulting spectrograms are converted into binary images, which are fed into the convolutional neural network. The network is trained using labeled examples of fall and nonfall activities. Our method employs high-level feature learning, which distinguishes it from previously studied methods that use heuristic feature extraction. The performance of the proposed method is evaluated by conducting several experiments on a set of radar return signals. We show that our method distinguishes falls from nonfalls with 98.37% precision and 97.82% specificity, while maintaining a low false-alarm rate, which is superior to existing methods. We also show that our proposed method is robust in that it successfully distinguishes falls from nonfalls when trained on subjects in one room, but tested on different subjects in a different room. In the proposed convolutional neural network, the hierarchical features extracted from the radar return signals are the key to understand the fundamental composition of human activities and determine whether or not a fall has occurred during human daily activities. Our method may be extended to other radar-based applications such as apnea detection and gesture detection.

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method.

Limited by the total length, the total number of the antenna units as well as their topology, the radar images always suffered from the sidelobe/grating lobe which severely impacts the quality of the radar images. In this article, a convolutional neural network (CNN)-based radar image-enhancing method is proposed. Using the original radar images as the input samples and using their corresponding ideal radar images with no sidelobe/grating lobe as the label to train the CNN. A well-trained CNN can suppress the sidelobe/grating lobe in the radar images. The structure of the specific CNN, the generation methods of the samples and the labels, the training procedure of the CNN, as well as some other detailed implementation strategies are specifically illustrated in this article. The proposed method is utilized to suppress the sidelobe/grating lobe in both the simulated and real recorded radar images. Compared to other existing methods, the proposed method is with better sidelobe/grating lobe suppressing performance and better robustness.

The development of an automotive surface recognition system is an important and yet unsolved task. In the current study we are considering a novel approach to surface classification based on the analysis of the real road surface images obtained using the 79 GHz imaging radar and demonstrate the advantage of millimeter wave radar for surface discrimination for automotive sensing. The proposed experimental technique in combination with a convolutional neural network provides high surface classification accuracy.

This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced.

Introduction. A radar image is an image obtained by remote sensing the earth's surface with a radar device. Radar images are characterized by background graininess caused by speckle noise, which should be filtered to improve the quality of radar images. The structure of speckle noise reduction filters often comprise one or more parameters to control the level of noise smoothing. The values of these parameters have to be selected experimentally. In works devoted to speckle noise filtering, the methods used for selecting filter paraments are rarely clarified.Aim. To present a methodology for selecting the parameters of multiplicative speckle noise filters on a radar image that are optimal in terms of the quality of the resulting image.Materials and methods. The article presents a method for determining the optimal parameters of speckle noise reduction filters. This method was applied to the most conventionally used filters. The search for optimal parameters and testing of the filters were carried out using a specially designed image, which contained the objects most frequently found on radar images. The structural similarity index (SSIM) metric was chosen as a metric that assesses the quality of filtration.Results. After determining the optimal (in terms of SSIM) parameters of speckle noise reduction filters, the filters were compared to select the best filters in terms of the quality of radar image processing. In addition, the operation of the filters under study was tested on images containing various types of objects, namely: large objects, small objects and sharp borders. Knowing which filter copes best with smoothing speckle noise in a particular area and what values of the variable parameters this requires, an optimal quality of radar images can be achieved. Filtering not only improves human perception of radar images, but also reduces the influence of speckle noise during their further processing (object detection, segmentation of areas, etc.).Conclusion. The proposed algorithm allowed optimal parameters for several speckle noise filters to be determined. The quality of filtration was assessed using an expert method (visually) by comparing images before and after filtration, differential images and one-dimensional image slices. The Frost filter and the anisotropic diffusion filter with optimal parameters showed the best processing quality according to the SSIM metric.

A conducting deep cavity is an essentially geometric structure, e.g., the inlet duct and the tail nozzle, of air vehicles and is a dominant scattering source of radar echoes. The scattering centers (SCs) induced by multiple reflections inside deep cavities are different from general SCs. Few references have studied the SC model for deep cavities. Therefore, a concise SC model is presented in this letter. In this model, an analytical expression for the projection distances of SCs along the line-of-sight (LOS) in deep cavities is derived by using the ray- tracing method. Rectangular cavities, cylindrical cavities, and a cavity composed of two cylindrical structures are investigated in this letter. The presented SC models are validated by a comparison between the radar images simulated by the model and those computed by a full-wave numerical method.

A backprojection-based synthetic aperture radar (SAR) processing approach for fast and accurate imaging of moving objects and stationary background is proposed. This divide-and-conquer backprojection approach allows for efficient detection of moving objects, estimation of their positions and velocities, and overlaying their focused images over the stationary background. Two algorithms are presented to exploit the tradeoff between the complexity and performance. The full 4-D phase space imaging algorithm provides the optimal performance under low signal-to-clutter conditions, while the adaptive detection and imaging leads to reduced complexity in more favorable scenarios. We show that in a single-channel SAR the conventional straight-line data collection path does not resolve the target’s velocity, therefore a curved path is assumed in the formulation and numerical examples.

In this article, a new sensing approach for the automatic detection and ranging of human targets in cluttered environments is detailed. The person detection does not require to preliminarily define a target threshold, thus minimizing the likelihood of false alarm events. Moreover, the proposed approach allows to resolve the artifacts due to the presence of multiple objects within the same radiation beam without increasing the system complexity and cost. A powerful graphical representation, denominated range-breathing graph, is exploited for quickly making the reader aware of the exact human location, employing the breathing rate as the key element for discerning between humans and non-human targets. The method is analytically described while exhaustive simulations are carried out for confirming the reliability and suitability of the method with regard to the state of art. The effectiveness of the range-breathing separation approach is demonstrated by using two different radar boards operating at the frequencies of 24 GHz and 122 GHz. The obtained results show the general validity of this approach for identifying humans in complex cluttered scenarios

In radar intrapulse modulation recognition through convolutional neural networks (CNN), the recognition rate vis-a-vis varying signal-to-noise ratios (SNRs) is utilized to analyze the performance of networks. However, three shortcomings exist in this realm, (1) Proportional increase in recognition rate up to 100%, due to SNR increase, leading to saturation. (2) The dependency of recognition rate on the test set. (3) Recognition rate failing to substantiate the increase in SNR and its manifestation in CNN performance enhancement. Foregone in view, this letter proposes an evaluation criterion to evaluate CNN performance to address the shortcomings above. We propose Grad-CAM position score-Internal Benchmark (GCPS-IB) to address (1), (2), and Grad-CAM position score-External Benchmark (GCPS-EB) to address (3). Finally, we utilize GCPS to evaluate and compare the performance of existing GoogLeNet and ResNet-18 to verify the efficacy. The proposed criterion establishes its efficacy by addressing the shortcomings mentioned above, hence augmenting the recognition rate.

This letter presents a robust data association method for fusing camera and marine radar measurements in order to automatically detect surface ships and determine their locations with respect to the observing ship. In the preprocessing step for sensor fusion, convolutional neural networks are used to perform feature extraction from camera images and semantic segmentation from radar images. The correspondences between the camera and radar image features are optimized using a pair of geometric parameters, and the positions of all the matched object features are determined. The proposed method enables robust data association even without accurate calibration and alignment between the camera and radar measurements. The feasibility and performance of the proposed method are demonstrated using an experimental dataset obtained in a real coastal environment.

Mobile robotics applications in outdoor environments now use intensively Global Positioning System (GPS). For localization or navigation operations, GPS has become an essential tool due to its ease of use, its precision, and its worldwide accessibility. The increase of autonomy level in mobile robotics implies a robust centimeter-level positioning, but the presence of natural (trees, mountains) or man-made obstacles (buildings) can degrade or prevent GPS signals reception. We present in this paper a solution for robots localization based on PELICAN microwave radar. PELICAN radar provides each second a panoramic image of the surrounding environment. These images are concatenated through a Simultaneous Localization And Mapping (SLAM) algorithm in order to build global maps of the traveled environments. The proposed solution computes the position and orientation of the robot through a real-time 3D matching between the current radar image and a pre-existing radar map constructed during an exploratory phase.

This letter presents a simple, lightweight, and ultrawideband, the very high frequency/ultrahigh frequency (VHF/UHF) open-sleeve dipole antenna array for airborne ice measuring radar. The proposed antenna array achieves a wide bandwidth of 300 MHz via mutual coupling from neighboring excited elements. A hollow aluminum tube is used for both dipole and parasitic elements to minimize the antenna's weight. The simulation results show that to achieve a bandwidth of 300 MHz with a peak realized gain up to 12 dBi in the frequency range from 170 to 470 MHz, the length of the dipole ( L dip) and parasitic elements ( L para) of elements 1 and 4 of the four-element antenna array need to be 670 mm and 200 mm, respectively. At the same time, elements 2 and 3 require an L dip of 640 mm and L para of 180 mm. Based on these dimensions, a four-element antenna array is fabricated and tested. The measured antenna performance results show a good agreement with the simulated results.

Presents a review of "Stimson’s Introduction to Airborne Radar, Third Edition" (Stimson, G.W., et al; 2014). There are four authors and nine expert contributors to this third edition of a classic introductory text. The original and key author, George Stimson, started the original manuscript when he was with Hughes Aircraft Company in the early 1960s to 1970s. As a private publication, the book was initially given away or sold at a nominal charge to Hughes’s customers and Stimson’s friends. He is a fine writer and has managed the production of the book for years. From the beginning, the book has had clear graphics and meticulous formatting of pages. This is an impressive book with a total of 52 chapters in 10 parts. It covers almost everything concerning airborne radar. It could serve as an airborne radar encyclopedia. Each chapter briefly introduces its subject, providing neither too much nor too little material. There are some mathematical equations, but all are at the algebra level and easy to grasp. The text includes many color graphs, drawings, and nice pictures, which makes it very easy to read. I would recommend this book to every radar designer, technician, operator, administrator, and program manager. The book covers the Doppler frequency of an aircraft, ground return, and return by a semiactive missile.

This article presents a robust hand-gesture recognition method via multidimensional feature representation and learning specifically designed for commercial frequency-modulated continuous wave (FMCW) multi-input multi-output (MIMO) millimeter-wave radar. First, the optimal configuration of the radar system parameters for the hand-gesture recognition scenario is investigated and a standard procedure to determine the system configuration is given. Then a moving scattering center model is proposed to represent the 3-D point cloud in the range–Doppler (RD)-angular multidimensional feature space. A scattering point detection and tracking algorithm is presented based on a set of motion constraints in terms of position, velocity, and acceleration. It is derived from the space-time continuity of a nonrigid target. Finally, a lightweight multichannel convolutional neural network (CNN) is designed to learn and classify multidimensional gesture features including radial RD and tangential azimuth–elevation. Extensive experiments are carried out with the developed system and a large data set is obtained to train and test the classifier. The results show that the proposed gesture recognition method can effectively distinguish gestures that are easily confused in the RD domain and achieve robust performances under various conditions.

An optoelectronic millimeter-wave (mm-wave) radar transmitter (TX) integrated circuit (IC) for multiple-input–multiple-output (MIMO) imaging radar in silicon photonics technology is presented. The MIMO radar system architecture in which the ICs shall be used features an optical local oscillator (LO) distributed over fiber to coherently operate mm-wave transceiver frontends. Optical LO distribution enables larger apertures and hence higher angular resolution for MIMO imaging radars. The optoelectronic TX frontend IC integrates all electronic and photonic components needed to receive the optical LO signal and to generate a transmit signal at mm-wave. It operates on both 1310- and 1550-nm LO wavelengths. The chip was designed in a pre-production photonic SiGe BiCMOS technology from IHP. Measured output power is 13 dBm at 67 GHz and 5 dBm at 77 GHz from −5-dBm optical modulation amplitude (OMA). The IC dissipates 900 mW from a single 3.6-V supply voltage.

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target‐dependent and target‐independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler‐time spectrograms and high‐range resolution profiles generated for diverse radar frequencies and wall characteristics in around‐the‐corner radar (ACR) scenarios. Additional experiments are performed on range‐enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal‐to‐noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single‐layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of −10 dB and label mismatch error of 50%.

High-Resolution Range Profile (HRRP) is effective in various Radar Automatic Target Recognition problems. Multi-scale techniques have been verified in aircraft target HRRP feature enrichment to ach...

Through-wall mapping is an emerging field of research with promising applications varying from search and rescue, to health care and to security. Radar is a valuable tool in this process, mainly due to its long wavelength, which can pass through construction materials. However, this capability comes at a price, namely multipath reflections caused by the environment, which can reduce the usability of the produced map considerably. This paper proposes methods to detect and isolate these multipath reflections, eventually leaving out a usable map of the enclosed environment. For this purpose, two different radars, one operating at 24GHz and the other at 76GHz, were evaluated in various wall configurations constructed inside an enclosed space using portable wall segments. The testing arena was probed from the outside with the radars mounted on the top of a differential drive mobile robot. The paper shows that the proposed methods are effective in eliminating multipath reflections and in building an accurate representation of the environment using the radars.

Obtaining the accurate and real-time state of surrounding objects is essential for automated vehicle planning and decision-making to ensure safe driving. In complex traffic scenarios, object occlusion, clutter interference, and limited sensor detection capabilities lead to false alarms and missed object detection, making it challenging to ensure the stability of tracking and state prediction. To address these challenges, in this study, we developed a robust multi-object detection and tracking method for moving objects based on radar and camera data fusion. First, the radar and camera perform target detection independently, and the detection results are correlated in the image plane to generate a random finite set with an object type. Then, based on the Gaussian mixture probability hypothesis density algorithm framework, the tracking process is improved using elliptic discriminant thresholds, an attenuation function, and simplified pruning methods. The experimental results demonstrate that the improved algorithm can accurately estimate the number and state of targets in object occlusion, measurement loss scenarios, and achieve robust continuous multi-object tracking. The proposed method could guide the design of safer and more efficient intelligent driving systems.

This work presents the implementation of a synthetic aperture radar (SAR) at 77 GHz, for automotive applications. This implementation is unique in the sense that it is a radar-only solution for most use-cases. The set-up consists of two radar sensors, one to calculate the ego trajectory and the second for SAR measurements. Thus the need for expensive GNSS-based dead reckoning systems, which are in any case not accurate enough to fulfill the requirements for SAR, is eliminated. The results presented here have been obtained from a SAR implementation which is able to deliver processed images in a matter of seconds from the point where the targets were measured. This has been accomplished using radar sensors which will be commercially available in the near future. Hence the results are easily reproducible since the deployed radars are not special research prototypes. The successful widespread use of SAR in the automotive industry will be a large step forward toward developing automated parking functions which will be far superior to today's systems based on ultrasound sensors and radar (short range) beam-forming algorithms. The same short-range radar can be used for SAR, and the ultrasound sensors can thus be completely omitted from the vehicle.

Monitoring urban land through satellite images has rapidly developed with the advent of modern technologies, and the increasing number of satellites plays a contributing role. While optical images have a high capability in urban monitoring, they still have some limitations, including their dependence on climatic conditions and spectral information, which lead to difficulty in making a distinction between bare land, buildings and other features. The impossibility of optical imagery at night is another issue that can make the land cover classification difficult. Synthetic aperture radar (SAR) allows imaging in all climatic conditions and at nighttime, with an ability to detect phenomena based on their geometry, roughness, and location, making the land cover classification much easier. In the present study, radar Sentinel-1 images with polarization VV and VH were used for the land classification in Tabriz. Sentinel-2 images for the same time were applied as a reference for the calibration and accuracy assessment. Maximum likelihood (ML) and support vector machine (SVM) algorithms were also employed for supervised classification. In both algorithms, the classification was performed in windows with different sizes once by the SAR backscattering coefficient (σ0) and then by combining the backscattering coefficients with the statistical data obtained from the texture. The results showed that the use of radar images only with backscattering intensity resulted in poor performance while using the gray-level co-occurrence matrix (GLCM) of texture features increased the accuracy. The transmitted frequencies of radar images have different redistributions to different phenomena. The numerical results obtained from the radar image classification show that using only the radar image redistribution led to low accuracies at both VV and VH polarization, but the use of the textural analysis significantly increased the accuracy of the classifications. The statistical results obtained from the ML and SVM classifications for radar images at VV and VH polarization indicated that the latter performed better than the former. When texture analysis was not used in the classes, the classification accuracy was low with kappa values of 0.37 and 0.42 for VV and VH polarization, respectively. The use of texture analysis and obtaining the optimum window size is increase the classification accuracy with a better performance for VH polarization. The SVM classification method with a kappa coefficient of 0.72% showed better performance than the ML one with a kappa coefficient of 0.61%. Conclusively, in the absence of Sentinel-2 datasets, Sentinel-1 images are good alternatives if the preserved texture information is available for the land cover classification. Results of this research are of great importance for developing the remote sensing methods and their techniques can be considered as progressive research in the domain of remote sensing sciences.

The issues of image fusion in a two-position small-size radar on-board operational monitoring system are considered. The aim of present research is to develop a method for fusion of images of the land surface based on data obtained from a multi-sensor spatially distributed on-board location system implemented on the basis of a UAV. The method of combining different-angle location images is implemented iteratively. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are highlighted using two image segmentation methods. The final result is a proposed method for information fusion from a two-position on-board small-sized radar system and an optical location system. The implemented method of fusion makes it possible to increase the information content, quality and reliability of information about the observed underlying surfaces and the physical objects detected on them. The practical significance of the results obtained lies in the formation of integral information in real time in the interests of environmental reconnaissance, monitoring in hard-to-reach and dangerous places for human life, as well as in order to promptly prevent natural and man-made emergencies.

Fast Fourier transform (FFT) is one of the fundamental signal processing algorithms widely used in radar applications. The Radon–Fourier transform (RFT) can be seen as an FFT generalization that can overcome some of its limitations. This work derives three spectral RFT (SRFT) based approaches to address major challenges of the multiple-input multiple-output automotive radars. First, two SRFT-based approaches are derived to increase maximal target detection range by mitigation of target migration in range and direction of arrival, jointly, and by multidwell integration processing, which increases the radar coherent integration time without compromising its detection update rate. Next, SRFT-based approach is proposed to address the cluster-to-track association problem that arises in multiple distributed target tracking scenarios that characterize automotive radar operation in dense urban environments.

International audience; The development of safe intelligent transportation systems (ITS) has driven extensive research to come up with efficient environment perception techniques with a variety of sensors. In short range settings, Ultra Wide-Band (UWB) radars represent a promising technology for building reliable obstacle detection systems as they are robust to environmental conditions. However, UWB radars suffer from a segmentation challenge: localizing relevant regions of interest (ROIs) within its signals. This article proposes a segmentation approach to detect ROIs in an environment perception-dedicated UWB radar. Specifically, we implement a differential entropy analysis to detect ROIs. We evaluate our technique on a benchmark of more than 47 thousands samples. The obtained results show higher performance in terms of obstacle detection compared to state-of-the-art techniques, and a stable robustness even with low amplitude signals.

Accurately capturing the burden surface information of a blast furnace (BF) is helpful to adjust the burden distribution matrix and improve the gas flow distribution, which is essential in the steel smelting industry. However, it is difficult to detect the burden surface because of the high temperature, high pressure, high dust and flame combustion environment condition inside the BF, in addition to the fluidization characteristics of the burden surface. With the fusion of high-temperature metallurgy, radar detection and image processing, a new BF surface deep-learning detection system based on energy weight was developed in this study to visualize the smelting status of a BF and digitize the information of the burden surface. First, the original burden surface echo spectrogram was captured using a frequency-modulated continuous wave radar with a high-temperature-resistant sensor. Second, the point cloud distribution map was constructed based on the principle of synthetic aperture radar (SAR) imaging. The coordinate transformation and matrix compensation were completed by the entropy weight method and Gamma correction to convert point cloud map to the burden surface grayscale image, constructing datasets for the network. Third, with a 3rd-order hourglass feature extraction network, a BF surface detection network based on the principle of YOLOv3 was designed and trained to predict the position of the burden surface in the grayscale image and track the changing trend of the burden line. The proposed method, which is tested on two steel plant datasets, achieved an average prediction precision of 99.74%. Finally, compared with the traditional burden line extraction algorithm, burden line oscillations and local outliers can be avoided by the proposed method. The continuous detection of the BF surface information was realized.

Existing Terahertz radar using CSAR mode has the problem of unsatisfactory imaging quality. The target and the noise are mixed in the full-aperture imaging result, which is difficult to use for automatic target recognition; and its sub-aperture-based imaging method not only does not completely solve this problem, but also brings about the problems of generating fake scattering points and the dependence on manual debugging. In view of the shortcomings of existing sub-aperture-based imaging methods, a Terahertz radar’s high-quality imaging method based on the sub-aperture is proposed by us. Among our research, 4 new methods, namely the sub-aperture division method which divides overlapping sub-apertures, the sub-aperture boundary point selection method, two new methods of selecting the sub-aperture boundary point, are used. The imaging method proposed by us further improves the signal-to-noise ratio of the target in the imaging result, and can be applied to targets with more complex shapes. Moreover, our imaging method solves the problems of generating fake scattering points and the dependence on manual debugging. After field test, our imaging method has better imaging quality than existing methods, and it is less dependent on manual debugging.

Image segmentation and classification of surfaces and obstacles in automotive radar imagery are the key technologies to provide valuable information for path planning in autonomous driving. As opposed to traditional radar processing, where clutter is considered as an unwanted return and should be effectively removed, autonomous driving requires full scene characterization. Hence, clutter carries necessary information for situational awareness of the autonomous platform and needs to be fully assessed to find the passable areas. In this paper, we proposed a method of automatic segmentation of automotive radar images based on two main steps: unsupervised image pre-segmentation using marker-based watershed transformation, followed by the supervised segmentation and classification of regions containing objects and surfaces based on the use of statistical distribution parameters. Several distributions were considered to characterize returns from specific region types of interest within the scene (denoted as classes) in calibrated radar imagery—the extracted distribution parameters were assessed for their ability to distinguish each class. These parameters were then used as features in a multivariate Gaussian distribution model classifier. Both the performances of the proposed supervised classification algorithm and the automatically segmented results were investigated using F1-score and Jaccard similarity coefficients, respectively.

This paper studies the methods of moving targets tracking based on a multi-beam tracking system in terahertz (THz) band, which is an initial attempt in this field. Taking the advantage of forming high resolution range profile of THz radar, a target geometric center tracking method is developed at first. However, the tracking performance is deteriorated by the severe angular glint and system disturbance. A further analysis on the experiment data finds a statistical correlation between the angular glint and the normalized amplitude variance, which is exploited to identify and exclude instable scatterers. A non-linear angular discrimination function is also established to deal with the anomalous measurement of the radar. Finally, an instable point exclusion method and a nonlinear angular discrimination method are proposed and combined to develop an improved tracking method, which can suppress angular glint and mitigate measuring perturbation, hence promoting the tracking performance. Proof-of-principle experiments are performed with a multi-beam tracking system working in 0.2 THz band. Tracking results of different targets demonstrate that the improved tracking method can effectively eliminate the tracking failure and significantly promote the tracking accuracy.