Deceptive jamming against synthetic aperture radar (SAR) is significant in defending against hostile reconnaissance and securing the region. Traditional jamming approaches primarily aim at single-imagery SAR, signal waveform type, multichannel, array, and degree of freedom. Since the video SAR (VideoSAR) system can enhance reconnaissance capability in detection, recognition, and perception in dynamic region of interest (DROI), it is imperative to devote to the relevant jamming discipline. To the best of our knowledge, it is the first time that a novel deceptive jamming perspective against VideoSAR system is proposed with simultaneously single-channel, single-band, and single-pass configurations. Frame-dependent principle of deceptive modulation against VideoSAR is derived from the video polar format algorithm (PFA). To obtain the VideoSAR deceptive jamming templates with diverse scattering features and high fidelity, a nonsubsampled Shearlet transform scattering characterization controlling approach is proposed for depicting the multidimensional intrinsic correlations of electromagnetic (EM) scattering behaviors. Three high-resolution airborne VideoSAR datasets are employed to confirm the effectiveness of the proposed deceptive jamming in anisotropy scenarios.

Machine learning-based automatic target detection in video synthetic aperture radar (VideoSAR) has great potential for raising the reconnaissance capability in dynamic region of interest (DROI). In this article, a novel systematic perspective, called low-rank plus sparse multiple-term decomposition (LRSMTD) for simultaneously single-channel, single-band, and single-pass (SCSBSP) VideoSAR configuration is proposed to track the ground defocusing targets. To address the target features of circular VideoSAR imaging, we extend the polar format algorithm (PFA) via exploiting a priori knowledge. In accordance with both the revealed imaging and imagery characteristics, we solve the systematic LRSMTD with proximal exchange-based alternating directions method of multipliers (PEADMM), which makes the process interpretable for defocusing target detection. Comprehensive circular SCSBSP airborne VideoSAR experiments reveal the superior detection performance of the systematic LRSMTD and its several subalgorithms with PEADMM, outperforming 11 state-of-the-art algorithms.

Barrage jamming technique with controllable jamming coverage against synthetic aperture radar (SAR) systems is of great importance in electronic countermeasures. However, it is still a difficulty for the jammer to accurately impose controllable two-dimensional (2-D) local jamming on the regions of interest (ROIs). In this respect, a new parameterized and large-dynamic-range precise controllable (PLDR-PC) jamming method has been proposed in this paper to assist in solving such problems. Based on the SAR imaging properties of linear frequency modulation (LFM) case, the range and azimuth modulation factors have been well designed to generate large dynamic controllable coverage of jamming signals with high 2-D processing gain. In such a context, the PLDR-PC technique can provide the optimal power allocation and considerably reduce the jamming power while still ensuring the satisfactory performance. The proposed PLDR-PC technique can improve the jamming efficiency and considerably reduce the exposure probability of the jammer. Moreover, to improve the barrage jamming performance, the parameter estimation error model is also established to determine the simple yet valid jamming strategy in practical implementations. Finally, extensive numerical simulations in comparison with the current jamming methods have been carried out to demonstrate the effectiveness and prospect of the PLDR-PC technique against airborne/spaceborne SAR systems.

A novel algorithm is proposed in this paper to focus the azimuth periodically gapped Synthetic Aperture Radar (SAR) raw data with suppressed artificial targets. The proposed method mainly consists of phase compensation in range frequency domain, subsequent sparse recovery via complex Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and focusing recovered SAR data with traditional SAR imaging algorithm. With phase compensation, the gapped data becomes sparser in range Doppler domain and complex FISTA is utilized to restore complete data from gapped data in range Doppler domain. Afterwards, the conventional SAR focusing algorithms are capable of coping with the recovered data. The proposed method focuses azimuth gapped data with lower artificial targets and performs faster than method based on complex Iterative Shrinkage- Thresholding Algorithm (ISTA). In experiments, point target simulation and real SAR data processing are utilized to demonstrate the validity of the proposed method.

Algorithms for high resolution spotlight Synthetic Aperture Radar (SAR) focusing typically face challenges of curved orbit and azimuth aliasing. To conquer these difficulties, a novel algorithm, which is named as Modified Generalized Omega-K (MGOK) algorithm, is proposed in this paper for focusing low-orbit high resolution spotlight spaceborne Synthetic Aperture Radar (SAR). The proposed method modifies the Generalized Omega-K algorithm with orbital state vectors and deramping-based approach in order to accommodate the scenario of Low Earth Orbit (LEO) high resolution spaceborne SAR. Experiments are performed on point target simulation for assessing the effectiveness of the proposed method. In simulation, the range and azimuth resolution can both obtain 0.5m with scene size of 4km by 4km.

Video synthetic aperture radar (VideoSAR) possesses the capability of imaging and continuously monitoring the scenario from a wide-aspect interval for enhancing the performance of information interpretation. In this paper, we propose a complete imaging scheme to achieve the unified video coordinate system in high-resolution airborne VideoSAR configuration. Comprehensive postprocessing video imaging (PPVI) framework built on range Doppler algorithm and range migration algorithm is elaborated especially in terms of complex measured data, which is divided into three parts for ensuring the stability of video background: full-aperture imaging, 2-D autofocus technique, and Doppler spectrum segmentation. Experimental results utilizing the measured airborne data have demonstrated the effectiveness of PPVI scheme for sequential VideoSAR formation.

The processing of synthetic aperture radar (SAR) echoes collected during radar antenna beam steering, such as in the staring spotlight, sliding spotlight, and TOPS operating modes, requires additional efforts due to the limited pulse repetition frequency (PRF). Subaperture processing is mostly used in these cases, but the complexity arising from subaperture dividing and recombination is preferably avoided. The two-step approach (TSA) is an elegant solution to full-aperture processing. Nevertheless, for high-resolution airborne and spaceborne SAR processing, the TSA is still faced with a number of difficulties, which does not occur, however, in subaperture processing. For instance, the high range bandwidth induces Doppler spectrum aliasing during the spectral analysis (SPECAN) stage of the original TSA. Moreover, the Doppler spectrum obtained in TSA, when inverse Fourier transformed back to the slow time domain, might also suffer from aliasing, which precludes the estimation and correction of the unknown residual motion error or tropospheric disturbance. In this article, we extend the TSA to address these problems and present a full-aperture processing framework to precisely focus the high-resolution airborne and spaceborne SAR data. Simulation of X-band spaceborne SAR echoes with transmission signal bandwidth of 3.6 GHz and coherent integration angle of 12.5° has validated the extended TSA (ETSA). Meanwhile, experimental X-band airborne SAR data with the same range and azimuth resolution are also processed using the ETSA, where the conventional autofocusing techniques have been smoothly incorporated.

Video synthetic aperture radar (VideoSAR) presents significant potential for improving the performance of information interpretation. Key frames represent the aspect-dependent electromagnetic energy, which frequently obscures other scattering physics dominated by specular returns. In this paper, we propose a vision-based background subtraction approach for capturing VideoSAR scattering key-frame information in simultaneously single-channel and single-pass configurations. The spatiotemporal key-frame extractor combines subaperture energy gradient with modified statistical and knowledge-based object tracker. It can robustly discriminate the scattering features of the alternation between transient persistence and disappearance. We evaluate the proposed method using several measured airborne data. Experimental results and performance comparison have demonstrated that the scattering key-frame extractor can achieve a high accuracy for VideoSAR summarization.

Video synthetic aperture radar (VideoSAR) provides a continuously multidimensional phase-history acquisition, proved to be significantly attractive in the remote sensing-based information extraction field. In this paper, we propose a 3-D structure-from-motion (SfM) retrieval method based on the cylinder model from circular VideoSAR sequences. First, VideoSAR imaging characteristics on the cylinder model are analyzed. Then, SfM retrieval method with geometric prior knowledge is proposed using robust shadow information. Finally, experimental results utilizing circular VideoSAR fragment published by the Sandia National Laboratories indicate that the proposed method can achieve a higher accuracy, hence, the validity has been demonstrated.

The Doppler shift effect results in some targets shadows in theirs actual position, and a strong correlation exists between adjacent frames of Video Synthetic Aperture Radar (VideoSAR) imagery. Based on the above rationale, a novel approach to moving targets shadow detection for high-frame-rate VideoSAR imagery sequence is presented. First, a fast preprocessing stage is essential in real applications, where the SIFT with RANSAC registration algorithm is employed to compensate for the changing background, and the CattePM model is used to suppress the speckle noise. Then, in order to separate the targets and the background automatically, a threshold segmentation algorithm, called maximizing the Tsallis entropy, is applied. Finally, background difference with three frame difference method implements the precise moving targets extraction. Experimental results utilizing VideoSAR imaging fragment show that multiple moving vehicles are detected effectively and, hence, the validity has been demonstrated.