基于压缩感知的SAR图像鲁棒编码传输

目的 尽管传统的联合信源信道编码方案可以获得高效的压缩性能,但当信道恶化超过信道编码的纠错能力时会导致解码端重构性能的急剧下降;为此利用压缩感知的民主性提出一种鲁棒的SAR图像编码传输方案,且采用了一系列方法提高该方案的率失真性能。方法 考虑到SAR图像丰富的边缘信息,采用具有更强方向表示能力的方向提升小波变换(DLWT)对SAR图像进行稀疏表示,且为消除压缩感知中恢复非稀疏信号时存在的混叠效应,采用了稀疏滤波方法保证大系数的精确恢复,在解码端采用了高效的Bayesian重建算法获得图像的高性能重建。结果 在同等码率下,与传统的联合信源信道编码方案CCSDS-RS相比,本文方案可以实现更加鲁棒的编码传输,当丢包率达到0.05时,本文方案DSFB-CS获得的重建性能明显要高于CCSDS-RS;与基于Bayesian重建算法TSW-CS的传统方案相比,本文方案可提高峰值信噪比(PSNR)3.9 dB。结论 本文方案DSFB-CS 实现了SAR图像的鲁棒传输,随着丢包率的上升,DSFB-CS获得的重建性能缓慢下降,保证了面对不稳定信道时,解码端可以获得相对稳定的重构图像。     

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.     

Compressive sensing SAR range compression with chirp scaling principle

Compressive sensing(CS) techniques offer a framework for the detection and allocation of sparse signal with a reduced number of measurements.This paper proposes a novel SAR range compression,namely compressive sensing with chirp scaling(CS-CS),achieving the same range resolution as conventional SAR approach,while using fewer range samplings.In order to realize accurate range cell migration correction(RCMC),chirp scaling principle is used to construct reference matrix for compressive sensing recovery.Additionally,error diagrams are designed for measurement of the performance of CS-CS,and some experiments of using real data are performed to deal with the errors caused by three conditions:SNR,sparsity and sampling.     

Sparse SAR imaging based on L1/2 regularization

In this paper,a novel method for synthetic aperture radar(SAR)imaging is proposed.The approach is based on L1/2 regularization to reconstruct the scattering field,which optimizes a quadratic error term of the SAR observation process subject to the interested scene sparsity.Compared to the conventional SAR imaging technique,the new method implements SAR imaging effectively at much lower sampling rate than the Nyquist rate,and produces high-quality images with reduced sidelobes and increased resolution.Also,over the prevalent greedy pursuit and L1 regularization based SAR imaging methods,there are remarkable performance improvements of the new method.On one hand,the new method significantly reduces the number of measurements needed for reconstruction,as supported by a phase transition diagram study.On the other hand,the new method is more robust to the observation noise.These fundamental properties of the new method are supported and demonstrated both by simulations and real SAR data experiments.     

Compressive sensing inverse synthetic aperture radar imaging based on Gini index regularization

In compressive sensing (CS) based inverse synthetic aperture radar (ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar (ISAR) imaging. Different from the traditional l ₁ norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation (SPSA), we use weighted l ₁ norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio (PSLR) and the reconstruction relative error (RE) indicate that the proposed method outperforms the l ₁ norm based method.     

A novel spaceborne SAR wide-swath imaging approach based on Poisson disk-like nonuniform sampling and compressive sensing

Since the range swath width in the conventional single channel spaceborne synthetic aperture radar(SAR)is restricted by the system parameters,there is a trade-off between the azimuth resolution and the swath width in order to satisfy the Nyquist sampling criterion.In this paper,we propose a novel spaceborne SAR wide-swath imaging scheme based on compressive sensing(CS)for the sparse scene.The proposed method designs a Poisson disk-like nonuniform sampling pattern in the azimuth direction,which meets the demand of wider swath by restricting the smallest time interval between any two azimuth samples,with the conventional sampling pattern preserved in the range direction.By a similar way to the processing procedure of spectral analysis(SPECAN)algorithm,the linear range migration correction(RMC)is realized while carrying out range compression,which can meet the demand for focusing with middle level resolution.To reduce the computation load of CS reconstruction,we propose a novel fast reconstruction algorithm based on nonuniform fast Fourier transform(NUFFT),which greatly reduces the computation complexity from O(2M N)to O(4N log N).Experiment results validate the effectiveness of the proposed methods via the point target simulation and the Radarsat-1 raw data processing in F2 mode.     

基于压缩感知的低数据率雷达采样与成像方法

传统的信号获取体制要求采样率大于两倍信号带宽,这使得高速率A/D转换成为经典超宽带高分辨雷达系统的瓶颈技术之一。压缩感知理论提供了一种低速率采样的信号精确采集和重构方式。本文基于压缩感知理论,提出一种新的雷达采样与成像方法。根据目标的散射特性,采用了基于小波变换的雷达目标稀疏表示方法;结合雷达成像原理,构造了基于Fourier束的最优测量矩阵。仿真实验表明,基于压缩感知的低数据率雷达采样与成像方法,能在数据率仅为传统系统数据率15%的条件下,获得良好的成像结果,尤其是能对弱小目标进行高分辨成像。本文所提的方法可为新体制高分辨率成像雷达系统的设计提供支持。     

基于地基合成孔径雷达的弱目标检测性能分析

合成孔径雷达（SAR）是一种主动微波成像遥感技术,弱目标检测与分类是其重要应用之一。鉴于弱目标的有效检测与SAR系统参数密切相关,为系统地研究弱目标的散射特性及其检测性能与SAR系统参数的关系,本文提出了一种基于地基SAR进行弱目标检测的实验研究方法。首先通过构建室内实验场景,获取了多频多极化原始SAR回波数据并进行成像处理;接着分析了不同工作频率、不同带宽条件下塑料材质目标以及干沙覆盖目标的后向散射特性;最后讨论了极化方式对检测性能的影响。本文的实验方法和结果有助于进一步利用极化干涉SAR信息进行弱目标检测研究。     

融合压缩感知和SVM的SAR变形目标识别算法

为降低合成孔 径雷达(Synthetic aperture radar, SAR)图像目标识别中目标方位角的影响,并提高对SAR变形目标的识别率,本文提出了一种基于压缩感知和支持向量机决策级融合的目标识别算法。该算法首先基于稀疏表征理论将SAR目标识别问题描述为压缩感知的稀疏信号恢复问题,然后基于稀疏系数分别进行目标类别判别与方位角估计。对样本进行姿态校正后,利用支持向量机分别对经过姿态校正和未经姿态校正的样本进行目标分类。最后采用投票表决法对3种算法的分类结果进行决策级融合。实验结果表明,基于压缩感知结果进行目标方位角估计有效,且随着训练样本数的增加,提出的决策级融合算法提高了SAR变形目标的识别率。     

Parameter estimation of moving targets in the SAR system with a low PRF sampling rate

In the synthetic aperture radar(SAR)system with low pulse repetition frequency(PRF)sampling,it is difficult for the motion parameters estimation of the moving targets,because of the Doppler spectrum ambiguity and Doppler centroid frequency ambiguity of the echo signals.Considering that moving targets are sparsely distributed in the observed scene,their positions and velocities can be reconstructed by using the compressed sensing(CS)technique.In this paper,the range-walk correction are implemented by the Keystone transform and the sparse range-walk correction(SRWC),then the CS technique is proposed to reconstruct motion parameters by processing the azimuth signals of the moving targets.Experiments using the simulated and real data are performed,and the results confirm the validity of the proposed method.     

合成孔径雷达及其干涉技术研究进展

合成孔径雷达(Synthetic aperturer adar,SAR)能够在全天候、全天时条件下对地面进行大范围测绘,是现代民用遥感和军事侦察中的重要手段。本文回顾了SAR及干涉合成孔径雷达(InSAR)技术的历史,叙述了SAR由非聚焦到完全聚焦,由光学处理到全数字式处理,由二维测绘到干涉三维测绘的发展历程。通过例举典型系统,介绍了国外机载、空载SAR和InSAR技术的现状,并对我国近年来在该领域取得的进展作了简要介绍。最后,本文给出对SIR—C／X—SAR采集的航天飞机SAR数据处理所得到的成像结果。     

High-resolution ISAR imaging of fast rotating targets based on pattern-coupled Bayesian strategy for multiple measurement vectors

Very high resolution inverse synthetic aperture radar (ISAR) imaging of fast rotating targets is a complicated task. There may be insufficient pulses or may introduce migration through range cells (MTRC) during the coherent processing interval (CPI) when we use the conventional range Doppler (RD) ISAR technique. With compressed sensing (CS) technique, we can achieve the high-resolution ISAR imaging of a target with limited number of pulses. Sparse representation based method can achieve the super resolution ISAR imaging of a target with a short CPI, during which the target rotates only a small angle and the range migration of the scatterers is small. However, traditional CS-based ISAR imaging method generally faced with the problem of basis mismatch, which may degrade the ISAR image. To achieve the high resolution ISAR imaging of fast rotating targets, this paper proposed a pattern-coupled sparse Bayesian learning method for multiple measurement vectors, i.e. the PC-MSBL algorithm. A multi-channel pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring range cells and correct the MTRC problem. The expectation-maximization (EM) algorithm is used to infer the maximum a posterior (MAP) estimate of the hyperparameters. Simulation results validate the effectiveness and superiority of the proposed algorithm.     

Compressed sensing SAR imaging based on sparse representation in fractional Fourier domain

Compressed sensing(CS)is a new technique of utilizing a priori knowledge on sparsity of data in a certain domain for minimizing necessary number of measurements.Based on this idea,this paper proposes a novel synthetic aperture radar(SAR)imaging approach by exploiting sparseness of echo data in the fractional Fourier domain.The effectiveness and robustness of the approach are assessed by some numerical experiments under various noisy conditions and different measurement matrices.Experimental results have shown that,the obtained images by using the CS technique depend on measurement matrix and have higher output signal to noise ratio than traditional pulse compression technique.Finally simulated and real data are also processed and the achieved results show that the proposed approach is capable of reconstructing the image of targets and effectively suppressing noise.     

基于改进Faster R-CNN的SAR船舶目标检测方法

合成孔径雷达（Synthetic Aperture Radar, SAR）船舶检测在海洋交通监控中发挥着重要作用,传统SAR目标检测算法一般利用目标与背景杂波之间的对比度差异进行检测,在近岸海域等复杂场景下检测效果较差。为了提高在复杂场景下的检测性能,本文提出一种基于改进Faster R-CNN的船舶检测方法,在分析不同特征分辨率对检测性能影响的基础上,结合VGG的思想与扩张卷积设计一个适用于SAR船舶目标检测的特征提取网络,以提升对小型船舶目标的检测能力。另外,根据sentinel-1A数据集中目标尺寸分布选取小尺寸anchor,并通过去除冗余anchor,将检测速度提升了一倍。在sentinel-1A数据集上的实验证明本文提出的算法能够快速、有效地从复杂场景SAR图像中检测出船舶目标。     

Waveform design and high-resolution imaging of cognitive radar based on compressive sensing

We introduce the compressive sensing(CS) theory for waveform design of cognitive radar,and then propose an algorithm for the high-resolution radar signal waveform and its corresponding imaging method based on the sparse orthogonal frequency division multiplexing-linear frequency modulation(OFDM-LFM) signal.We first present the principle of spectrum synthesis and high-resolution imaging based on OFDM-LFM signals.Then,we propose the spectrum-sparse waveform design criterion and the reconstruction algorithm for a highresolution range profile(HRRP) based on CS.Based on this,we analyze in detail the relationship between the scattering characteristics of the target and the parameters of the designed signal,and we construct the feedback of the target characteristics on the waveforms.Therefore,the "cognitive" function of radar can be achieved by adaptively adjusting the waveform with the target characteristics.Simulations are given to validate the effectiveness of the proposed algorithm.     

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.     

Performance improvement in multi-ship imaging for ScanSAR based on sparse representation

There is always a compromise between unambiguous wide-swath imaging and high cross-range resolution owing to the constraint of minimum antenna area for conventional single-channel spaceborne synthetic aperture radar(SAR)imaging.To overcome the inherent systemic limitation,multi-channel SAR imaging has been developed.Nevertheless,this still suffers from various problems such as high system complexity.To simplify the system structure,a novel algorithm for high resolution multi-ship ScanSAR imaging based on sparse representation is proposed in this paper,where the SAR imaging model is established via maximum a posterior estimation by utilizing the sparsity prior of multi-ship targets.In the scheme,a wide swath is generated in the ScanSAR mode by continuously switching the radar footprint between subswaths.Meanwhile,high crossrange resolution is realized from sparse subapertures by exploiting the sparsity feature of multi-ship imaging.In particular,the SAR observation operator is constructed approximately as the inverse of conventional SAR imaging and then high resolution SAR imaging including range cell migration compensation is achieved by solving the optimization.Compared with multi-channel SAR imaging,the system complexity is effectively reduced in the ScanSAR mode.In addition,enhancement of the cross-range resolution is realized by incorporating the sparsity prior with sparse subapertures.As a result,the amount of data is effectively reduced.Experiments based on measured data have been carried out to confirm the effectiveness and validity of the proposed algorithm.     

Hierarchical distributed compressive sensing for simultaneous sparse approximation and common component extraction

Simultaneous sparse approximation is a generalization of the standard sparse approximation, for simultaneously representing a set of signals using a common sparsity model. Distributed compressive sensing (DCS) framework has utilized simultaneous sparse approximation for generalizing compressive sensing to multiple signals. DCS finds the sparse representation of multiple correlated signals from compressive measurements using the common + innovation signal model. However, DCS is limited for joint recovery of a large number of signals since it requires large memory and computational time. In this paper, we propose a new hierarchical algorithm to implement the joint sparse recovery part of DCS more efficiently. The proposed approach is based on partitioning the input set and hierarchically solving for the sparse common component across these partitions. The numerical evaluation of the proposed method shows the decrease in computational time over DCS with an increase in reconstruction error. The proposed algorithm is evaluated for two different applications. In the first application, the proposed method is applied to video background extraction problem, where the background corresponds to the common sparse activity across frames. In the second application, a common network structure is extracted from dynamic functional brain connectivity networks.     

基于多尺度压缩感知金字塔的极化干涉SAR图像分类

提出了一种新的基于多尺度压缩感知(Compressed sensing, CS)金字塔的分类方法, 用于合成孔径雷达(Synthetic aperture radar, SAR)图像的分类. 首先通过原始图像上的小波平滑和特征提取构建多尺度极化干涉特征空间, 然后利用压缩感知提取每一个尺度上图像子块的观测域特征并在数据域重建稀疏特征, 最后组合多尺度的稀疏特征生成最终用于分类的多尺度金字塔表达. 针对稀疏编码和一般金字塔算法的局限性, 提出了基于压缩感知和多尺度金字塔的方法, 利用观测矩阵降低特征维数的优势的同时, 对SAR图像的纹理特征进行不同尺度的分析. 在国内首批极化干涉SAR数据上的实验证明了上述算法的有效性.     

匀速运动目标的聚束SAR成像特征分析

运动目标的合成孔径雷达(SAR)成像特征是SAR/GMTI系统中进行运动目标检测和成像的基础.以往研究都是在两维可分离条带SAR成像算法条件下讨论目标运动对成像的影响,而很少考虑高分辨聚束SAR成像算法处理后运动目标的成像特征.本文推导了两种典型高分辨聚束SAR成像算法(RMA和PFA)处理后的动目标误差谱表达式,并在此基础上从目标几何定位误差、残留距离徙动和散焦等方面给出了完整的聚束SAR运动目标响应特征分析.最后通过仿真数据处理验证了分析结果.