This article presents a survey of recent research on sparsity-driven synthetic aperture radar (SAR) imaging. In particular, it reviews 1) the analysis and synthesis-based sparse signal representation formulations for SAR image formation together with the associated imaging results, 2) sparsity-based methods for wide-angle SAR imaging and anisotropy characterization, 3) sparsity-based methods for joint imaging and autofocusing from data with phase errors, 4) techniques for exploiting sparsity for SAR imaging of scenes containing moving objects, and 5) recent work on compressed sensing (CS)-based analysis and design of SAR sensing missions.

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Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

Ieee transactions® on aerospace and electronic systems

We investigate the problem of a monostatic pulse-Doppler radar transceiver trying to detect targets sparsely populated in the radar's unambiguous time-frequency region. Several past works employ compressed sensing (CS) algorithms to this type of problem but either do not address sample rate reduction, impose constraints on the radar transmitter, propose CS recovery methods with prohibitive dictionary size, or perform poorly in noisy conditions. Here, we describe a sub-Nyquist sampling and recovery approach called Doppler focusing, which addresses all of these problems: it performs low rate sampling and digital processing, imposes no restrictions on the transmitter, and uses a CS dictionary with size, which does not increase with increasing number of pulses P. Furthermore, in the presence of noise, Doppler focusing enjoys a signal-to-noise ratio (SNR) improvement, which scales linearly with P, obtaining good detection performance even at SNR as low as - 25 dB. The recovery is based on the Xampling framework, which allows reduction of the number of samples needed to accurately represent the signal, directly in the analog-to-digital conversion process. After sampling, the entire digital recovery process is performed on the low rate samples without having to return to the Nyquist rate. Finally, our approach can be implemented in hardware using a previously suggested Xampling radar prototype.

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Sub-Nyquist Radar via Doppler Focusing

Deceptive jamming suppression with frequency diverse MIMO radar

Traditional radar sensing typically employs matched filtering between the received signal and the shape of the transmitted pulse. Matched filtering (MF) is conventionally carried out digitally, after sampling the received analog signals. Here, principles from classic sampling theory are generally employed, requiring that the received signals be sampled at twice their baseband bandwidth. The resulting sampling rates necessary for correlation-based radar systems become quite high, as growing demands for target distinction capability and spatial resolution stretch the bandwidth of the transmitted pulse. The large amounts of sampled data also necessitate vast memory capacity. In addition, real-time data processing typically results in high power consumption. Recently, new approaches for radar sensing and estimation were introduced, based on the finite rate of innovation (FRI) and Xampling frameworks. Exploiting the parametric nature of radar signals, these techniques allow significant reduction in sampling rate, implying potential power savings, while maintaining the system's estimation capabilities at sufficiently high signal-to-noise ratios (SNRs). Here we present for the first time a design and implementation of an Xampling-based hardware prototype that allows sampling of radar signals at rates much lower than Nyquist. We demonstrate by real-time analog experiments that our system is able to maintain reasonable recovery capabilities, while sampling radar signals that require sampling at a rate of about 30 MHz at a total rate of 1 MHz.

Sub-Nyquist radar prototype: Hardware and algorithm

Recently, the idea of compressed sensing (CS) has been used in radar system, and the concept of compressed sensing radar (CSR) has been proposed in which the target scene can be sparsely represented in the range-Doppler plane. With sufficiently incoherent transmission waveform, the target scene can be reconstructed by the technique of CS. With the idea that the transmission waveform can adapt in response to the operational information in cognitive radar system, we propose the notion of adaptive compressed sensing radar (ACSR) whose transmission waveform and sensing matrix can be updated by the target scene information fed back by the recovery algorithm. The methods for optimizing the transmission waveform and sensing matrix separately and simultaneously are both presented to decrease the cross correlations between different target responses. The principle for an ACSR system to synthesize the transmission waveform and sensing matrix matched to the target scene is also investigated. This novel ACSR system offers more degrees of freedom than classical radar system and better recovery performance than the CSR system.

Adaptive Compressed Sensing Radar Oriented Toward Cognitive Detection in Dynamic Sparse Target Scene

An effective electronic counter-countermeasures (ECCM) technique for a moving target indication/pulsed Doppler (MTI/PD) radar system against velocity deception jamming is proposed. A novel radar signaling strategy is outlined based on variations of the initial phases of the transmitted pulses in pulse repetition interval (PRI) domain. It makes the Doppler spectrum of the false targets, created by a digital radio frequency memory (DRFM) repeat-back jammer, form a notch around the Doppler frequency of the true one. The penalty function and corresponding algorithm for designing adaptive initial phases are given. We also present an approach of the multi-channel matched filter processing to estimate the manner, by which a DRFM repeat jammer operates, and the parameters of the false targets. The working flow of an MTI/PD radar for countering velocity deception jamming is investigated. Simulation results show the effectiveness of the above methods.

New Antivelocity Deception Jamming Technique using Pulses with Adaptive Initial Phases

This letter deals with a novel transmitted scheme for a multisubcarrier frequency multiple-input multiple-output (MIMO)-synthetic aperture radar (SAR) system, which aims at suppressing the intrapulse repeater mainlobe-jamming and immensely improving the dynamic range of the receiver. To this end, due to the multisubcarrier frequency transmission scheme, the mixed signal with true target signal and repeater jamming can be separated by a well-designed spatial-frequency filter. Thus, the range and direction of arrival (DOA) information is accurately estimated without interrupting the normal work of the radar. Furthermore, to further improve the orthogonality of transmitted waveform, the design of a phase-coded LFM waveform pair exhibiting both low cross correlation energy (CCE) and low peak to sidelobe ratios (PSLRs), is considered. Besides, to handle the resulting nondeterministic polynomial (NP) hard problem, an alternating direction multiplier method (ADMM) based optimization method is employed. Compared with the traditional jamming suppression method, the proposed method improves the degree of freedom (DOF) from the carrier frequency domain, and it has the capability to suppress the intrapulse repeater mainlobe-jamming. Finally, detailed simulation experiments are carried out to verify the practicability and effectiveness of the newly proposed transceiver schemes.

A Novel Intrapulse Repeater Mainlobe-Jamming Suppression Method With MIMO-SAR

On the remote sensing images of marine synthetic aperture radar (SAR), ship targets often occupy only a small part of an image, and the rest are all sea and coastal backgrounds. Existing neural networks based on SAR ship detection often directly detect an entire SAR image, which consumes a large amount of computing resources. In this article, a new marine SAR ship detection network, called multilevel background removing–you only look once X (MLBR-YOLOX), is proposed. First, a new plug-and-play module, called standalone spatial patch detector, is proposed to predetect the position of ships and filter out most of the sea backgrounds at the source image level. Second, a deep spatial feature detector is presented for detecting deep semantic features of the output of the backbone module to further reduce the computational cost of the neck and head modules. Finally, the original YOLOX network is adopted to locate and classify the pre-detected results. Experimental results on the SAR ship detection dataset and the high-resolution SAR images dataset indicate that the detection performance of MLBR-YOLOX is close to that of YOLOX, but the computational complexity is merely 23.97% and 12.50% of YOLOX's, respectively. Moreover, the experiment conducted on a large-scene Sentinel-1 SAR image illustrates that the proposed network has good migration application capability.

MLBR-YOLOX: An Efficient SAR Ship Detection Network With Multilevel Background Removing Modules

Aiming to resolve azimuth-dense targets in interference environments, the radar needs to have the ability of single snap echo angular super-resolution with anti-interference. To solve the problem, the angular super-resolution algorithm based on single snap echo while anti-interference with blocking matrix method is studied for active phased array radar (APAR) in this paper. Since the super-resolution ability of the conventional MUSIC algorithm and iterative adaptive algorithm (IAA) algorithm are limited in single snap echo, the iterative re-weighted least squares (IRLS) algorithm under p-norm constraint is proposed. Further, the near main lobe interference suppression ability is enhanced by the adaptive diagonal loading method. The performance differences of IAA and IRLS algorithms for single-target detection and double-target angular super-resolution are analyzed in detail by numerical simulation on three scenes of no interference, side-lobe interference, and near-main-lobe interference. The simulation results show that the proposed algorithm can effectively solve the problem of target angle estimation and super-resolution based on single sample echo in an interference environment, including near main-lobe interference.

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Jindong Zhang

Papers

Adaptive Compressed Sensing Radar Oriented Toward Cognitive Detection in Dynamic Sparse Target Scene

New Antivelocity Deception Jamming Technique using Pulses with Adaptive Initial Phases

A Novel Intrapulse Repeater Mainlobe-Jamming Suppression Method With MIMO-SAR

MLBR-YOLOX: An Efficient SAR Ship Detection Network With Multilevel Background Removing Modules

Angular Super-Resolution of Multi-Channel APAR in Interference Environments