Bingchen Zhang

Fast Compressed Sensing SAR Imaging Based on Approximated Observation

In recent years, compressed sensing (CS) has been applied in the field of synthetic aperture radar (SAR) imaging and shows great potential. The existing models are, however, based on application of the sensing matrix acquired by the exact observation functions. As a result, the corresponding reconstruction algorithms are much more time consuming than traditional matched filter (MF)-based focusing methods, especially in high resolution and wide swath systems. In this paper, we formulate a new CS-SAR imaging model based on the use of the approximated SAR observation deducted from the inverse of focusing procedures. We incorporate CS and MF within an sparse regularization framework that is then solved by a fast iterative thresholding algorithm. The proposed model forms a new CS-SAR imaging method that can be applied to high-quality and high-resolution imaging under sub-Nyquist rate sampling, while saving the computational cost substantially both in time and memory. Simulations and real SAR data applications support that the proposed method can perform SAR imaging effectively and efficiently under Nyquist rate, especially for large scale applications.

Sparse microwave imaging: Principles and applications

This paper provides principles and applications of the sparse microwave imaging theory and technology. Synthetic aperture radar (SAR) is an important method of modern remote sensing. During decades microwave imaging technology has achieved remarkable progress in the system performance of microwave imaging technology, and at the same time encountered increasing complexity in system implementation. The sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theory, new system and new methodology of microwave imaging. Based on classical SAR imaging model and fundamental theories of sparse signal processing, we can derive the model of sparse microwave imaging, which is a sparse measurement and recovery problem and can be solved with various algorithms. There exist several fundamental points that must be considered in the efforts of applying sparse signal processing to radar imaging, including sparse representation, measurement matrix construction, unambiguity reconstruction and performance evaluation. Based on these considerations, the sparse signal processing could be successfully applied to radar imaging, and achieve benefits in several aspects, including improvement of image quality, reduction of data amount for sparse scene and enhancement of system performance. The sparse signal processing has also been applied in several specific radar imaging applications.

Adaptive Total Variation Regularization Based SAR Image Despeckling and Despeckling Evaluation Index

We introduce a total variation (TV) regularization model for synthetic aperture radar (SAR) image despeckling. A dual-formulation-based adaptive TV (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV-regularization-based image restoration model has a good performance in preserving image sharpness and edges while removing noises, and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. A despeckling evaluation index (DEI) is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of different sizes of a pixel. Experimental results show that the proposed ATV method can effectively suppress SAR image speckles without compromising the edge sharpness of image features according to both subjective visual assessment of image quality and objective evaluation using DEI.

Random noise SAR based on compressed sensing

Recent theory of compressed sensing (CS) suggested that exact recovery of an unknown sparse signal can be achieved from few measurements with overwhelming probability. In this paper, we combine CS technology with a random noise SAR and proposed the concept of random noise SAR based on CS. The block diagram of the radar system and the collected data processing procedure was presented. Theoretic analysis show that the sensing matrix of the random noise SAR exhibits good restricted isometry property (RIP).When the target scene is sparse or sparse in any basis, the random noise radar based on CS can get high accuracy image by collecting far less amount of echo data than traditional noise radar does. The conclusions are all demonstrated by simulation experiments.

Multi-channel SAR imaging based on distributed compressive sensing

The rapid development of compressive sensing (CS) shows that it is possible to recover a sparse signal from very limited measurements. Synthetic aperture radar (SAR) imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem. For multichannel SAR imaging based on CS, each channel requires sufficient samples for separate imaging and the total number of samples could still be large. We propose an imaging algorithm based on distributed compressive sensing (DCS) that reconstructs scenes jointly under multiple channels. Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene, but also exploits the correlation among channels. It requires significantly fewer samples than multi-channel SAR imaging based on CS. If multiple channels offer different sampling rates, DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.

Experimental results and analysis of sparse microwave imaging from spaceborne radar raw data

Sparse microwave imaging is a novel radar framework aiming to bring revolutions to the microwave imaging according to the theory of sparse signal processing. As compressive sensing (CS) is introduced to synthetic aperture radar (SAR) imaging in recent years, the current SAR sparse imaging methods have shown their advantages over the traditional matched filtering methods. However, the requirement for these methods to process the compressed range data results in the increase of the hardware complexity. So the SAR sparse imaging method that directly uses the raw data is needed. This paper describes the method of SAR sparse imaging with raw data directly, presents the analysis of the signal-to-noise ratio (SNR) in the echo signal by combining the traditional radar equation with the compressive sensing theory, and provides the tests on 2-D simulated SAR data. The simulation results demonstrate the validity of the SNR analysis, and the good performance of the proposed method while a large percentage of the raw data is dropped. An experiment with RadarSat-1 raw data is also carried out to show the feasibility of processing the real SAR data via the method proposed in this paper. Our method is helpful for designing new SAR systems.

MIMO SAR processing with azimuth nonuniform sampling

This paper analyses ambiguity suppression caused by multiple-input multiple-output (MIMO) SAR azimuth nonuniform samplings. Two methods are analyzed: azimuth spectrum reconstruction algorithm and minimum mean square error (MMSE) imaging algorithm. The azimuth spectrum reconstruction algorithm can reconstruct the scene fine resolution, while the nonideal orthogonality of multi-channel encoding waveforms causes azimuth ambiguous in SAR imaging. The MMSE imaging algorithm can perfectly reconstruct, while it requires high SNR.

DLSLA 3-D SAR imaging algorithm for off-grid targets based on pseudo-polar formatting and atomic norm minimization

This paper concerns the imaging problem for downward looking sparse linear array three-dimensional synthetic aperture radar (DLSLA 3-D SAR) under the circumstance of sparse and non-uniform cross-track dimensional virtual phase centers configuration. Since the 3-D imaging scene behaves typical sparsity in a certain domain, sparse recovery approaches hold the potential to achieve a better reconstruction performance. However, most of the existing compressive sensing (CS) algorithms assume the scatterers located on the pre-discretized grids, which is often violated by the off-grid effect. By contrast, atomic norm minimization (ANM) deals with sparse recovery problem directly on continuous space instead of discrete grids. This paper firstly analyzes the off-grid effect in DLSLA 3-D SAR sparse image reconstruction, and then introduces an imaging method applied to off-gird targets reconstruction which combines 3-D pseudo-polar formatting algorithm (pseudo-PFA) with ANM. With the proposed method, wave propagation and along-track image reconstruction are operated with pseudo-PFA, then the cross-track reconstruction is implemented with semidefinite programming (SDP) based on the ANM model. The proposed method holds the advantage of avoiding the off-grid effect and managing to locate the off-grid targets to accurate locations in different imaging scenes. The performance of the proposed method is verified and evaluated by the 3-D image reconstruction of different scenes, i.e., point targets and distributed scene.创新点下视稀疏线性阵列三维合成孔径雷达(DLSLA 3-D SAR)常常由于跨航向的稀疏阵列安装条件受限等因素出现等效相位中心缺失和非均匀分布的情况,造成跨航向稀疏非均匀采样。对于具有稀疏性的3-D SAR成像场景,压缩感知(CS)方法能够在稀疏非均匀采样情况下获得良好的重构效果。然而,大多数CS算法都是基于离散假设,即假设散射点准确位于离散网格上;当真实散射点与离散网格不重合时,CS算法的重构效果将会受到网格偏离现象(off-grid effect)的影响。与离散的CS算法不同,原子范数最小化方法(ANM)直接在连续域上对稀疏信号进行恢复,不受网格偏离现象的影响。本文首先分析了DLSLA 3-D SAR跨航向稀疏重构时存在的网格偏离现象,然后提出了伪极坐标变换与原子范数最小化结合的成像算法。该算法首先通过距离压缩对波传播方向成像,然后对航迹向和跨航向进行伪极坐标变换,并通过傅里叶变换实现航迹向成像,然后在跨航向利用原子范数最小化方法进行成像。本文提出的方法能够在不同的成像场景中避免网格偏离现象、获得精确的成像结果。不同成像场景(点目标和分布式目标场景)的仿真实验成像结果验证了本文算法的有效性。

Radar Change Imaging With Undersampled Data Based on Matrix Completion and Bayesian Compressive Sensing

Matrix completion (MC) is a technique of reconstructing a low-rank matrix from a subset of matrix elements. This letter proposes an approach for change imaging from undersampled stepped-frequency-radar data via MC. We demonstrate that MC can be used to reconstruct the unknown samples. Based on the recovered full sample data, we then perform the estimation of the change image using a Bayesian compressive sensing (BCS) approach. Compared with existing compressive sensing (CS)-based techniques, which are sensitive to noise and clutter, the proposed method reduces the false-alarm rate and achieves sparser change imaging, which is due to more available data offered by MC and our explicit consideration of clutter and additive noise in the imaging procedure. The effectiveness of the proposed method is validated with experimental results based on raw radar data.

Influence factors of sparse microwave imaging radar system performance: approaches to waveform design and platform motion analysis

Sparse microwave imaging radar is a newly developed concept of microwave imaging system, which tries to combine the traditional radar imaging system with sparse signal processing theories, achieving the aim of reducing the complexity of microwave imaging systems and enhancing the system performance. In this paper, we introduce some basic concepts of sparse signal processing theory, and then apply it to the traditional radar imaging system to get the mathematical model of sparse microwave imaging system. We analyze the factors that determine the performance of sparse microwave imaging radar, including scene, waveform and platform. According to the radar model, we analyze how these factors influence the radar system and how to optimize them. Simulation results of the sparse microwave imaging radar system are also provided.