Achieving joint communication and imaging via uplink transmission presents significant challenges due to the unknown communication signal and the coupling of communication and sensing echoes. In this paper, a joint uplink communication and imaging system with only one RF chain is proposed, where a reconfigurable intelligent surface (RIS) is used to assist the base station (BS) to achieve joint signal detection and imaging (JSDI). Aiming to enhance the transmission gain in the desired directions and generate the required radiation pattern in the imaging region of interest (RoI), a RIS phase optimization problem is formulated, which is high dimensional and non-convex. We transform the original problem into a more tractable form by introducing auxiliary variables. Then, a backpropagation (BP) based Batch Gradient Descent (BGD) for both continuous and discrete phase cases is developed. Numerical results show that the proposed RIS phase optimization method achieves a controllable trade-off between communication and imaging performance and reveals a favorable range of the weighting factor where imaging performance can be significantly improved without causing a severe loss in symbol detection. Additionally, the echo decoupling problem is tackled using a Bayesian approach with factor graph techniques, which involve joint maximum a posteriori (MAP) probability estimation and adaptive sparse Bayesian learning (SBL). The proposed decoupling method asymptotically approaches the lower bound of communication and imaging. Numerical results also show that communication performance can be enhanced by utilizing imaging echoes compared to other benchmark communication systems.

For base stations (BSs) with integrated sensing and communication (ISAC) capabilities, interference from neighboring BSs not only degrades their communication performance but also increases sensing errors. While beamforming is commonly employed to suppress interference from undesired directions, this approach overlooks the potential sensing gain offered by target-reflected interference signals. In this paper, we investigate a scenario where a BS employs adaptive beamforming to leverage target-reflected interference for enhanced sensing performance. Notably, we deliberately preserve the line-of-sight interference component to enable accurate estimate of the interference symbols, enabling interference utilization without prior knowledge of the pilot of interference signals. Simulation results validate the effectiveness of the proposed approach in improving sensing performance through beamforming-enabled exploitation of interference.

In distributed airborne radar systems, the linear target localization method using time of arrival (TOA) is extensively studied due to its excellent robustness and simplicity in computation. Quadratic or higher-order terms related to range are disregarded in conventional linear methods, which results in compromised localization accuracy under high noise conditions. Considering the position uncertainty of moving platforms and a more realistic prior variance in target position estimation, an improved planar approximation algorithm is proposed, which refines target location iteratively. The spherical equations are approximated in each iteration as planar equations involving the current estimated target position. The error introduced by the planar approximation is recalculated and compensated for, enabling high-accuracy target location. In simulations, the proposed algorithm can reduce the average number of iterations by approximately 20%, and achieves a localization accuracy improvement of over 5 dB when the standard deviation of distance measurement error exceeds 1 km.

Distributed multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) provides a new paradigm for radar imaging, which utilizes multiple distributed sensors to improve imaging performance. However, synchronization errors have a significant impact on imaging quality in these systems. The transmitted and received echo signals exhibit reciprocity, which can be exploited to estimate synchronization errors. By comparing echoes between different sensors, the synchronization errors could be estimated and compensated. This work presents a synchronization error-resistant imaging algorithm for distributed MIMO-SAR systems. First, the synchronization errors are estimated in the range domain by comparing the reciprocal echo signal pairs. Then, the errors are compensated during a fast back-projection (BP) based SAR imaging process. The effectiveness of the proposed algorithm has been verified by experiments.

Mitigation of interference between base stations (BSs) is a significant challenge in integrated sensing and communication (ISAC) systems, particularly in noncooperative deployments. This letter investigates the scenario where an ISAC-enabled BS experiences interference from downlink (DL) transmission of another noncooperative BS (NBS). We observe that target-reflected interference contains valuable information, motivating its exploitation to enhance sensing capability. However, precise symbol estimation of NBS signals is infeasible without pilot information. To address this, we propose a novel iterative reconstruction-elimination algorithm (IREA) that derives a phase-ambiguous estimate of NBS signals through an efficient one-dimensional search, thereby enabling both interference mitigation and target information extraction from the reflected interference signals. Simulations demonstrate significant improvements in target detection and localization performance through our interference exploitation method.

High frame-rate imaging of synthetic aperture radar (SAR), known as video SAR, has received much research interest these years. It usually operates at extremely high frequency and even THz band as a technical tradeoff between high frame rate and high resolution. As a result, video SAR system always suffers from limited functional range due to strong atmospheric attenuation of signals. This article attempts to present a new high frame-rate collaborative imaging regime in microwave frequency band based on swarm of unmanned aerial vehicles (UAVs). The spatial degrees of freedom are employed to shorten the synthetic time and thus improve the frame rate. More specifically, the long synthetic aperture is split into multiple short sub-apertures, and each UAV-borne radar implements short sub-aperture imaging in a short time. Then, the accelerated fast back-projection algorithm is employed to fuse multiple sub-images to produce an image with high azimuth resolution.To implement the collaborative working of swarm of UAV-borne radars, a suitable orthogonal waveform is selected and a useful spatial configuration of the swarm is designed to compensate for the effect of the orthogonal waveform on imaging. Simulation results have been presented to highlight the advantages of collaborative imaging using swarm of UAV-borne radars.

Target detection in remote sensing images has received much research interest in the past years. Many saliency detection methods and constant false alarm rate (CFAR) detection methods have been proposed, which work effectively in simple scenes and high-resolution images, and unfortunately, they deteriorate in cases of complex scenes or low imaging quality. Multisource remote sensing images become much more accessible these years, and they provide redundant and complementary information about the area of interest. This article presents a detection method for stationary or slow-moving vehicle targets in complex scenes using optical and synthetic aperture radar (SAR) images. We utilize the contrast pyramid (CP) algorithm to fuse optical-SAR images and employ distinct methods to detect the saliency probabilities of optical, SAR, and fused images, respectively. Then, we calculate potential target areas based on these saliency probability maps and employ a region-growing method within these areas to extract the contours of possible targets. Finally, a CFAR detection method is used to obtain accurate vehicle targets. Experimental results demonstrate that the proposed method significantly improves the detection performance of stationary and slow-moving targets in complex scenes.

Compressive sensing (CS) enables high-resolution inverse synthetic aperture radar (ISAR) imaging with limited measurements. However, these methods reconstruct images via iterative optimization, resulting in a high computational load. Recently, convolutional neural networks (CNNs) have been used to perform super-resolution ISAR imaging in real time, where high-resolution images are necessarily used as ground truth. However, the desired high-resolution images are not reliable in practice. This letter presents an unsupervised CNN-based framework for super-resolution ISAR imaging. The well-trained CNN can directly produce high-resolution ISAR images in real time. Moreover, the network is trained in an unsupervised manner, which is suitable for practical applications. Furthermore, a pseudo$\ell _{0}$-norm has been used as the sparse constraint for the exact image reconstruction. The proposed approach has been used to process the real ISAR data, and the experimental results are convincing.

This article proposes an algorithm framework recently developed for video synthetic aperture radar (SAR), and presents the processing results of the airborne data collected by a W-band radar. This framework aims to accelerate time domain algorithm and produce SAR video that has no change of view angle, which is highly desired in target tracking. The presented framework includes a few modified processing algorithms, where the strategy of subaperture recursion is proposed. The modified acceleration algorithm is used to avoid the interpolation and to adapt to high squint angle without rotation of image grids. Additionally, the conventional autofocus method has been improved to deal with urban data that usually contain strong scatterers. The algorithm framework can achieve SAR video without rotation of view angle with a lower computational load compared to other existing time domain methods, which has been verified on airborne real data. Its processing efficiency becomes impressive if the apertures are highly overlapped in some cases that requires very high frame rate.

Track-Before-Detect (TBD) algorithm has been used to track weak moving target shadow in video synthetic aperture radar (SAR), where strong maneuverability may deteriorate tracking performance. Fortunately, the Doppler characteristic of target can additionally provide velocity guidance for tracking. This article presents ground moving target indication (GMTI) results of the multi-channel video SAR raw data, and a tracking approach is proposed based on the Doppler supervision in an improved dynamic programming-based TBD (DP-TBD) framework that uses a dual-domain merit function. Both the sequential high-resolution SAR images and low-resolution Range-Doppler (RD) spectra are used, and the clutter in RD spectrum is suppressed by using an adaptive displaced phase center antenna (ADPCA) technique. Fine states can be searched in dual-domain through resampling from random expansion in state initialization and transition. The inverse shadow amplitude and Doppler energy of the same potential target are simultaneously integrated to determine whether the target exists. The proposed Dual-DP-TBD can deal with the tracking of a time-varying number of targets in successive measurements. Compared to other TBD algorithms, this approach has fewer missing alarms and false alarms thanks to precise velocity estimation constrained from diverse features both in SAR image and RD spectrum.

Sea-surface small target detection is challenging for maritime radar. Unfortunately, conventional detection methods are often limited to complex marine environment and low signal-to-clutter ratio (SCR). This article presents a multiframe detection approach for sea-surface small target by using deep convolutional neural network. The moving targets can be reconstructed and detected from the sequential range–Doppler (RD) spectra. A two-step detection framework is proposed, where the intraframe and interframe detections are achieved using the differences in features and interframe correlations between the moving target and sea clutter, respectively. The proposed approach has been verified on both the simulated and real sea-surface small targets, which shows better detection performance than the conventional multiframe detection algorithms. Additionally, this approach exhibits acceptable generalization ability.

Video synthetic aperture radar (SAR) has been found very useful in ground moving target indication (GMTI) and tracking. The dynamic shadows in video SAR imagery sequences indicate the real positions of moving targets, which can be utilized in target detection and tracking. Unfortunately, the shadow-based method often fails when the shadows are not sufficiently developed. On the other hand, the traditional energy-based GMTI methods exhibit performance degradation when SAR images of a moving target are distorted or smeared. Neither of these two methods can stand alone to provide robust detection and tracking of moving targets. This article presents a joint processing framework for video SAR GMTI and tracking by combining the target shadow and echo energy information, which effectively lowers the false alarm and miss-detection rate. This approach is very suitable for real-time surveillance of ground moving targets in a single-channel video SAR system. The proposed approach has been verified by using the simulated and the real video SAR data.

It has long been recognized that synthetic aperture radar (SAR) images suffer in many applications from the speckle noise. Video SAR has a high frame rate of imaging and contains redundant information among frames. The temporal redundancy in video SAR images has been found useful for suppressing the speckle noise. However, the motion and the local differences between frames make it difficult to employ the temporal redundancy to suppress speckle noise. This article presents a video SAR image despeckling framework based on a new unsupervised training strategy referred to as DualNoise2Noise. This developed framework consists of a registration network and a denoising network. The registration network first compensates for the motion between two adjacent frames in video SAR in real time. After image registration, two adjacent frames with random speckle noise can be considered as the observations of the same region with local differences. The denoising network adopts the DualNoise2Noise training strategy to suppress speckle noise by using the temporal redundancy and to remove the negative impact caused by the local differences. The proposed approach has been used to process the real video SAR data, and the experimental results are convincing.

The spatially and temporally correlated sea clutter with phase information is valuable for marine radar applications. The major difficulty of coherent sea clutter modeling is the generation of the continuous phases. This article presents a new phase retrieval approach for modeling the correlated complex sea clutter based on unsupervised neural networks. The unsupervised short-term and long-term neural networks have been developed for the phase retrieval on different term scales. Both these networks have the same input layer and feature extraction module, and however, the number of output neurons is different. The amplitude sea clutter series and the desired Doppler spectrum are fed into the network in parallel, and their features are extracted by two parallel bidirectional long short-term memory (Bi-LSTM) networks which sufficiently utilize the correlations of sea clutter data. These features are concatenated and fused by a residual network (ResNet). The phases can be successfully obtained by constraining to the desired Doppler spectrum and the given amplitudes of sea clutter series. This proposed approach has been verified by the measured Ice Multiparameter Imaging X-Band (IPIX) radar data, and it can precisely model the complex sea clutter with specified statistic characteristics and Doppler properties. The amplitude root mean square error (RMSE) between the obtained and measured Doppler spectra is only 1.5065 with the interval between adjacent frames equals to 32. The RMSE of Doppler central frequency and spectrum width is 6.9306 and 1.2293 Hz, respectively. It shows robustness with the change of range resolution and interval.

Visual Emotion Analysis (VEA) aims at finding out how people feel emotionally towards different visual stimuli, which has attracted great attention recently with the prevalence of sharing images on social networks. Since human emotion involves a highly complex and abstract cognitive process, it is difficult to infer visual emotions directly from holistic or regional features in affective images. It has been demonstrated in psychology that visual emotions are evoked by the interactions between objects as well as the interactions between objects and scenes within an image. Inspired by this, we propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images. To mine the emotional relationships between distinct objects, we first build up an Emotion Graph based on semantic concepts and visual features. Then, we conduct reasoning on the Emotion Graph using Graph Convolutional Network (GCN), yielding emotion-enhanced object features. We also design a Scene-Object Fusion Module to integrate scenes and objects, which exploits scene features to guide the fusion process of object features with the proposed scene-based attention mechanism. Extensive experiments and comparisons are conducted on eight public visual emotion datasets, and the results demonstrate that the proposed SOLVER consistently outperforms the state-of-the-art methods by a large margin. Ablation studies verify the effectiveness of our method and visualizations prove its interpretability, which also bring new insight to explore the mysteries in VEA. Notably, we further discuss SOLVER on three other potential datasets with extended experiments, where we validate the robustness of our method and notice some limitations of it.

Video synthetic aperture radar (SAR) has been found to be very valuable for detection and tracking of slow moving targets and for observing changes over short time periods. Shadows produced by target motion in sequential radar images can be used to detect targets themselves. This article presents a framework for shadow-aided moving target detection using deep neural networks (DNNs) in video SAR. The faster region-based convolutional neural network (Faster-RCNN) is first used to detect shadows in a single frame, and then all the detections in each frame are filtered by the improved density-based clustering algorithm to mitigate false alarms. The missing alarm in detection can be suppressed by using the designed bi-directional long-short-term memory (Bi-LSTM) network. The proposed approaches have been tested by both the simulated and the real data, and a comparison to classical processing is given. This article reveals that the DNN techniques are very suitable for video SAR moving target detection.

This article presents a novel radar imaging approach through an electronically displaced-phase-center antenna (eDPCA). In this approach, a reflectarray is used to directly scan a beam across a parabolic reflector, where the antenna phase center is displaced electronically. The proposed eDPCA-based imaging radar can provide a very high frame rate and does not depend on the motion between the target and the radar platform. The feasibility of the eDPCA-based imaging radar is investigated and validated through a designed example eDPCA at 157 GHz and the simulation experiments for a point target and a complex target, respectively. The effects of practical impairments such as errors in phase centers and position-dependent fluctuations in antenna patterns on the imaging quality are examined. Furthermore, by applying the precompensation method suggested in this article, the imaging procedure could be performed by using classic synthetic aperture radar (SAR) algorithms.

The image processing for diving highly squinted synthetic aperture radar (SAR) mounted on a maneuvering platform with nonlinear trajectory is challenging due to the spatial variance in the range envelope along azimuth, which is caused by the inherent range dependence of squint angle, platform acceleration, and the range cell migration (RCM). In order to deal with the spatial variance that leads to defocusing in SAR imaging, a range perturbation approach is proposed in the frequency domain to mitigate the azimuth-dependent RCM curvatures, so that the RCM can be uniformly compensated for in the 2-D frequency domain. Moreover, this approach is totally interpolation free, and thus the computation load is reduced dramatically. The effectiveness of the proposed approach is confirmed and demonstrated via simulations.

An efficient and accurate inverse extended omega-K algorithm (IEOKA) is proposed to simulate synthetic aperture radar (SAR) raw data with trajectory deviations. Different from the traditional inverse omega-K algorithm that assumes an ideal flight trajectory, the IEOKA not only recovers the range cell migration accurately but also considers the motion errors including both range and phase errors due to the use of inverse extended Stolt interpolation. Furthermore, the azimuth dependence of the motion errors is discussed. A beam division method based on frequency division technique is presented to generate the azimuth-dependent phase error more accurately. The accuracy and effectiveness of the proposed algorithm have been verified using the generated SAR raw data consisting of the azimuth-dependent motion error.

Due to the skew data support region (DSR) of the 2-D wavenumber spectrum for high-squint synthetic aperture radar (HS-SAR), the conventional w-k algorithm cannot fully utilize the DSR and thus degrades the resolution if simply taking a rectangle region. Meanwhile, for the small-aperture data, direct azimuth imaging in the distance domain will lead to serious aliasing. A modified ω-k algorithm is derived in this paper to solve these problems. The maximum usage of DSR is achieved by the coordinate rotation. As for the azimuth dependence, the method of azimuth resampling is used to get the uniform focusing. Different from the traditional w-k method, the modified w-k algorithm focuses the small-aperture data in the azimuth wavenumber domain by SPECAN processing, which avoids padding a large number of zeros when imaging in the azimuth distance domain. Simulated and real-data results show the validity and effectiveness of the presented algorithm.

The bistatic synthetic aperture radar (SAR) data, which are converted into equivalent monostatic data by proper preprocessing, can be processed by standard monostatic focusing algorithms. The dip moveout (DMO) approach, which is derived from seismic data processing, converts the bistatic data into equivalent monostatic data by a short time-domain Rocca's smile operator. A 2D exact point-target (PT) reference spectrum is derived in this letter for the tandem bistatic configuration. The geometry-based bistatic formulation is shown to be actually equivalent to Rocca's smile operator, although they are derived from the pure SAR and geophysics points of view, respectively. Moreover, the new PT spectrum can be extended to deal with azimuth-invariant bistatic SAR data. Interpretations on the equivalent monostatic range wavenumber are presented in this letter, which help understand the conversion from the radar signal processing viewpoint.

An along-track interferometric synthetic aperture radar (SAR) can be used for ground moving target indication (GMTI) by comparing two SAR images obtained at different observation times. Different geometries of the two observations bring the decorrelation noise, which will degrade the detection performance. For bistatic SARs, the decorrelation theory is quite different from that for monostatic ones. This paper deals with the coherence between two complex SAR images formed by two along-track bistatic SARs with different baseline lengths. Using the single scattering model, the coherence between the two echoes collected by the two receivers is investigated, and the full-coherence conditions are derived. Then, a new method based on range-azimuth prefiltering is proposed to improve the coherence of complex image pairs. As the precise prefiltering is complicated, its three approximate implementations are given. The effects of prefiltering on SAR images are also analyzed. Finally, simulation results validate the effectiveness of the proposed method.

In recent years, bistatic synthetic aperture radar (BiSAR) has attracted the attention of many radar researchers. It is well known that the slant range history of BiSAR is the sum of two square-rooted terms, which correspond to the transmitting and receiving slant ranges, respectively. For a point target in the SAR scene, it is quite difficult, if not impossible, to obtain an analytic formula to describe the target echo data in the 2-D frequency domain without any approximation by using the conventional stationary phase method, which makes it very difficult to develop fast-focusing algorithms for BiSAR. In this paper, based on the concept of an instantaneous Doppler wavenumber and by defining a new variable called half quasi-bistatic angle, an analytic formula of the point target response in the spectral domain is developed for BiSAR with parallel trajectory (referred to as parallel BiSAR for simplicity). Relying on a first-order Taylor expansion of the above formula with respect to the parameter called the sum of closest distances on the swath center, a bistatic range migration algorithm is proposed for any azimuth-shift-invariant BiSAR data processing. Simulation results have confirmed the effectiveness of the proposed novel approach.