Cross-resolution enhancement for airborne high-squint radar (AHSR) imagery is mathematically equivalent to the ill-conditioned problem of inverse-scattering reconstruction. Although a variety of inversion methods with regularization can be introduced to advance the field of AHSR imagery, they turn out to be computationally intensive when extended to 2-D (range and cross-range dimension) image formulation due to the range-by-range calculation for the space-variant inversion operators over the full range swath. To tackle the problem of efficiency, this article presents a low-complexity inverse-scattering strategy. Our underlying idea is to equalize the space-variant Doppler centroid embedded in an inversion operator for a reference range cell using Doppler centroid compensation. With the proposed strategy, the necessary computational complexity required for 2-D AHSR inverse-scattering reconstruction can be significantly reduced by requiring only the calculation of the inversion operator, independently of the number of range cells. Our experimental assessment, conducted using both the simulation and real data, demonstrates that our proposed inverse-scattering strategy offers preferable computational reduction in the task of inverse-scattering reconstruction for 2-D AHSR imagery without resolution loss.

Fast Inverse-Scattering Reconstruction for Airborne High-Squint Radar Imagery Based on Doppler Centroid Compensation

This editorial of the special issue titled “Synthetic Aperture Radar (SAR) Techniques and Applications”, reviews the nineteen papers selected for publication. The proposed studies investigate different aspects of SAR processing including signal modelling, simulation, image analysis, as well as some examples of applications. The papers are grouped according to homogeneous subjects, then objectives and methods are summarised, and the more relevant results are commented.

Special Issue "Synthetic Aperture Radar (SAR) Techniques and Applications".

Cross-resolution enhancement for airborne high-squint radar (AHSR) imagery is mathematically equivalent to the ill-conditioned problem of inverse-scattering reconstruction. Although a variety of inversion methods with regularization can be introduced to advance the field of AHSR imagery, they turn out to be computationally intensive when extended to 2-D (range and cross-range dimension) image formulation due to the range-by-range calculation for the space-variant inversion operators over the full range swath. To tackle the problem of efficiency, this article presents a low-complexity inverse-scattering strategy. Our underlying idea is to equalize the space-variant Doppler centroid embedded in an inversion operator for a reference range cell using Doppler centroid compensation. With the proposed strategy, the necessary computational complexity required for 2-D AHSR inverse-scattering reconstruction can be significantly reduced by requiring only the calculation of the inversion operator, independently of the number of range cells. Our experimental assessment, conducted using both the simulation and real data, demonstrates that our proposed inverse-scattering strategy offers preferable computational reduction in the task of inverse-scattering reconstruction for 2-D AHSR imagery without resolution loss. 

Moving target indication (MTI) based on space–time adaptive processing (STAP) has been widely used in airborne radar due to its ability for clutter suppression performance. However, the existing MTI methods suffer from the problems of insufficient training samples and low detection probability in a non-homogeneous clutter environment. To address these issues, this paper proposes a novel deep learning framework to improve target indication capability. First, combined with the problems of target indication caused by the non-homogeneous clutter, the clutter-plus-target training dataset was modeled by simulation, where various non-ideal factors, such as aircraft crabbing, array errors and internal clutter motion (ICM), were considered. The dataset considers various realistic situations, making the proposed method more robust. Then, a five-layer two-dimensional convolutional neural network (D2CNN) was designed and applied to learn the clutter and target characteristics distribution. The proposed D2CNN can predict the target with a high resolution to implement an end-to-end moving target indication (ETE-MTI) with a higher detection accuracy. In this D2CNN, the input was obtained by the clutter-plus-target angle-Doppler spectrum with a low-resolution estimated only by a few samples. The label was given by the target angle-Doppler spectrum with a high-resolution obtained by the target’s exact angle and Doppler. Thirdly, the proposed method used a few samples to improve the target indication and detection probability, which solved the problem of insufficient samples in the non-homogeneous clutter environments. To elaborate, the proposed method directly implements ETE-MTI without the support of the conventional STAP algorithm to suppress the clutter. The results verify the validity and the robustness of the proposed ETE-MTI with a few samples in the non-homogeneous and low signal-to-clutter ratio (SCR) environments.

/pdf/end-to-end-moving-target-indication-for-airborne-radar-using-3pxewdke.pdf

End-to-End Moving Target Indication for Airborne Radar Using Deep Learning

This paper addresses the problem of ground moving target relocation (GMTR) for airborne dual-channel wide-area radar systems. The monopulse technique can be utilized to perform GMTR. However, in real conditions, the GMTR performance degrades greatly due to the effect of channel mismatch. To tackle this problem, prior knowledge of the antenna pattern information is fully utilized to improve the GMTR performance, and a knowledge-aided GMTR algorithm (KA-GMTR) for airborne dual-channel wide-area radar is proposed in this paper. First, the GMTR model for the two receiving channels is analyzed. The channel mismatch model is constructed, and its expression is derived. Then, the channel mismatch phase error is well estimated by exploiting the prior antenna pattern information based on the least squares (LS) method. Meanwhile, the knowledge-aided monopulse curve (KA-MPC) is derived to perform the direction of arrival (DOA) estimation for potential targets. Finally, KA-GMTR, based on the KA-MPC, is performed to estimate the azimuth offsets and relocate the geometry positions of the potential targets when channel mismatch occurs. Moreover, the target relocation performance is analyzed, and the intrinsic reason that degrades the target relocation accuracy is figured out. The performance assessment based on airborne real-data, also in comparison to the conventional GMTR method, has demonstrated that our proposed KA-GMTR algorithm offers preferable target relocation results under channel mismatch scenarios.

Knowledge-Aided Ground Moving Target Relocation for Airborne Dual-Channel Wide-Area Radar by Exploiting the Antenna Pattern Information

This paper deals with the problem of high cross-range resolution Doppler beam sharpening (DBS) imaging for airborne wide-area surveillance (WAS) radar under short dwell time situations. A knowledge-aided DBS (KA-DBS) imaging algorithm is proposed. In the proposed KA-DBS framework, the DBS imaging model for WAS radar is constructed and the cross-range resolution is analyzed. Since the radar illuminates the imaging scene continuously through the scanning movement of the antenna, there is strong spatial coherence between adjacent pulses. Based on this fact, forward and backward pulse information can be predicted, and the equivalent number of pulses in each coherent processing interval (CPI) will be doubled based on the autoregressive (AR) technique by taking advantage of the spatial continuity property of echoes. Finally, the predicted forward and backward pulses are utilized to merge with the initial pulses, then the newly merged pulses in each CPI are utilized to perform the DBS imaging. Since the number of newly merged pulses in KA-DBS is twice larger than that in the conventional DBS algorithm with the same dwell time, the cross-range resolution in the proposed KA-DBS algorithm can be improved by a factor of two. The imaging performance assessment conducted by resorting to real airborne data set, has verified the effectiveness of the proposed algorithm.

Knowledge-Aided Doppler Beam Sharpening Super-Resolution Imaging by Exploiting the Spatial Continuity Information.

Airborne radar can be used to surveillance wide area and monitor interesting targets, which makes it a good choice for military and civil applications. However, the ground clutter is spread over a region in Doppler due to the movement of the platform, and potential slowly moving target may be obscured by the heavy clutter. To solve this problem, a knowledge-aided Space-time Adaptive Processing (KA-STAP) algorithm is proposed. In particular, the prior knowledge of clutter range-variance property as well as the information extracted from the radar data have been fully utilized. Firstly, the blocked sample selection strategy in range is utilized to decrease the range-variance effect. Then, low threshold CFAR strategy is exploited to choose proper samples in the secondary data, which is quite useful to eliminate the potential targets or jammings. By exploiting this prior knowledge information, the accuracy of the estimated covariance matrix can be well improved. Therefore, the clutter suppression performance can be enhanced in heterogeneous environment. Finally, the performance of different clutter suppression methods are analyzed by resorting to airborne experimental results, which further confirms the effectiveness of the proposed algorithm.

Knowledge-aided Space Time Adaptive Processing for Airborne Radar in Heterogeneous Environments

This letter addresses the problem of target relocation in wide-area ground moving target indication system. The antenna pattern information is exploited as prior knowledge to enhance the performance of airborne radar. Firstly, the ideal target relocation model for moving targets based on the dual-channel radar system is analysed and the mismatch model of the receiving channel is constructed. Then the reason for poor relocation performance is analysed and the channel error model is constructed. Based on this fact, the problem of target relocation is converted into a problem of channel phase estimation. Moreover, a least-squares method is utilised to estimate the phase error by exploiting the real antenna pattern information and a modified mono-pulse curve (MPC) is derived. Finally, the real geometry location of the moving target can be achieved with the modified MPC. Airborne radar experiments are given to verify the effectiveness of the proposed algorithm.

Efficient knowledge‐aided target relocation algorithm for airborne radar

The problem of real-time imaging in synthetic aperture radar (SAR) system is addressed here. Sub-aperture processing algorithm is usually used in the engineering applications. However, high azimuth sidelobes and edge aliasing problem usually occur in conventional sub-aperture algorithm, which degenerates the quality of the SAR image. To solve this problem, the reason for high azimuth sidelobes and edge aliasing problem is analysed, and a modified sub-aperture processing algorithm is proposed. The proposed algorithm minimises the effect of the phase discontinuity to improve the SAR image quality. Therefore, the azimuth sidelobes can be improved, and the edge aliasing phenomenon will be eliminated. Real measured airborne SAR datasets demonstrate the effectiveness of the proposed algorithm.

Modified real-time sub-aperture processing algorithm for airborne high-resolution SAR

Detecting and resolving the space group targets in the main beam of radar is an urgent requirement for the air-defense and anti-missile radar system. Ground-based radar, as an important instrument for space surveillance, can be used to detect and track the space targets like grouped aircrafts, warheads and the decoys of the missiles. However, it is difficult to detect and resolve the dense targets due to the limit of the radar resolving power. To solve this problem, a space group targets detecting and resolving algorithm based on ultra-low sidelobe filtering is proposed. By exploiting the convex optimization into the pulse-Doppler radar, the problem of ultra-low sidelobe is converted into the problem of optimization. The key of this algorithm is to minimize the peak to sidelobe level (PSL) of the range sidelobes with a constraint of signal to noise ratio (SNR) loss. Then the ultra-low sidelobe filtering results are used to detect and resolve the space group targets in the main beam. Numerical and experimental results demonstrate the effectiveness of the proposed algorithm.

Space Group Targets Detecting and Resolving Algorithm via Ultra-low Sidelobe Filtering

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