一种基于混沌调频信号的稀疏雷达成像

稀疏雷达成像是指将稀疏信号处理理论引入雷达成像并有机结合而成的微波成像新方法。混沌调频信号是一种基于混沌映射产生的类噪声信号,与采用线性调频信号的雷达相比,它的波形之间互不相干且具有随机性,提升了雷达的隐蔽性能;其模糊函数形状为图钉形,增强了稀疏雷达成像中观测矩阵的列不相关性。利用支持分布式运算的交替方向法对稀疏场景进行重构,仿真实验表明,基于混沌调频信号的稀疏雷达成像可以在降低信号采样率的条件下对原始场景进行准确重建。

基于L_(1/2)正则化理论的稀疏雷达成像

稀疏雷达成像旨在远低于奈奎斯特率采样下对稀疏场景实施高分辨率微波成像.本文概述作者在解决这一问题上的系统探索与创新实践.核心贡献包括:提出L_(1/2)正则化理论作为新的稀疏雷达成像理论,提出不直接基于雷达观测而基于雷达回波模拟算子重构的稀疏雷达成像新模型,提出以3D相变图分析为依据的稀疏雷达成像系统设计方法等.根据新的理论、模型和设计方法,研制了首部稀疏雷达原理样机并开展了机载实验.实验验证了所提新理论、新模型与新方法的正确性和可行性,展示广阔应用前景.

组合字典下超宽带穿墙雷达自适应稀疏成像方法

针对现有超宽带穿墙雷达稀疏成像算法大多只采用点目标稀疏基表示模型和稀疏优化的正则化参数不能被自适应调整以及目标位置不在划分网格上带来虚假像的问题,该文提出一种基于贝叶斯证据框架的自适应稀疏成像方法。该方法首先利用组合字典独立稀疏表示场景中的点目标和扩展目标,然后在建立的偏离网格稀疏表示模型的基础上分层最大化各参数的似然函数,用第1层推理结合共轭梯度算法估计组合字典的各稀疏表示系数,用第2层推理估计正则化参数和目标的偏离网格量,最终通过迭代优化参数的设置得到问题的求解。仿真和实验结果表明,该方法不仅同时自适应增强穿墙场景中的点目标和扩展目标,还消除了偏离网格目标引起的虚假像。

超宽带穿墙雷达偏离网格目标稀疏成像方法

在超宽带穿墙雷达成像应用中,由于目标成像空间的稀疏性表示可以采用压缩感知在较少数据采集下获得高分辨、低旁瓣的成像结果。然而,这些稀疏成像方法要求真实目标必须位于预设的网格点上才能保证较好的成像质量,否则会出现严重的虚假目标像。本文提出一种基于修正贝叶斯压缩感知的偏离网格目标稀疏成像方法。该方法对偏离网格目标的感知矩阵在预设网格点进行一阶泰勒级数展开,把真实目标与网格点之间的偏移量视为稀疏贝叶斯模型的参数,然后通过构造的联合概率密度函数利用最大期望方法联合估计目标散射系数和偏移量,保证了目标真实像的准确恢复。仿真和实验结果表明,该方法通过校正目标偏离网格引起的模型误差有效改善了传统稀疏重建方法的成像质量。

星载GMTI稀疏阵雷达的STAP研究

针对稀疏阵的欠采样会引起动目标检测盲区的问题,基于星载GMTI(ground moving targets indica-tion)稀疏线阵雷达,分析了多载频稀疏阵雷达的系统模型和空时自适应处理(space time adaptive processing,STAP)的基本原理,考虑了非周期阵列对减少盲区的作用。仿真结果表明采用多载频模式可有效改善稀疏引起的动目标检测盲区。

基于最小冗余的天基稀疏阵雷达多载频STAP研究

天基GMTI(Ground Moving Target Indication)稀疏阵雷达的模糊性问题受到广泛关注。针对角度模糊、多普勒模糊导致的动目标检测盲区问题,本文将最小冗余思想应用于雷达阵列设计,在子阵列数目受限的情况下,给出两种最小冗余阵列配置,能够解决无模糊测速范围和速度分辨率之间的矛盾,改善稀疏导致的角度模糊问题。在存在多普勒模糊的情况下,结合多载频技术,建立了多载频最小冗余阵雷达系统模型。通过仿真证明空-时-频三维自适应处理比单频STAP(Space-Time Adaptive Processing)性能明显提高,能够有效消除多普勒模糊和提高动目标检测性能。

基于进化PSO算法的稀疏捷变频雷达波形优化

为了提高稀疏捷变频(sparse frequency agility, SFA)雷达信号在稀疏重构中的精度和稳定性,提出一种基于进化粒子群优化(particle swarm optimization, PSO)算法的SFA雷达信号的优化设计。首先,推导了SFA雷达的信号模型和稀疏重构时的字典矩阵。然后,以最小化SFA雷达信号对应字典矩阵的相关性为目标函数,稀疏载频情况下有效带宽和有效跳频间隔为约束条件,建立优化模型。最后,通过进化PSO算法求解得到最优载频序列。仿真结果表明,所提算法在满足稀疏性约束的条件下,能够有效提高字典矩阵的正交性,保证稀疏重构信号的精度和可靠性。

稀疏激光雷达与可见光/红外成像系统的标定方法

激光雷达与成像系统之间的位姿标定是激光点云与图像像素进行融合的前提。目前主流的离线标定方法中,普通棋盘格标定板用于64线及以上的激光雷达时效果较好,而用于16线激光雷达时由于其数据稀疏而导致误差较大。而且,涉及红外成像系统的标定时,需要特制的棋盘格来获得发射率差异。本文针对稀疏激光雷达点云数据较少的问题,研究了可以同时标定激光雷达与可见光、红外成像系统的方法,设计了菱形九孔标定板,并提出几何约束损失函数来优化特征点的坐标。最后,分别使用红外和可见光成像系统与16线激光雷达进行标定,实验结果表明,平均重投影误差均在3个像素之内,取得了较好的效果。本文方法还能用于稀疏激光雷达与可见光-红外多波段成像系统的标定。

基于参数估计的下视稀疏阵列三维SAR运动误差补偿和成像处理方法

当载机存在偏航角速度时,载机航线会偏离理想航线,对稀疏阵列下视3维合成孔径雷达(DLSLA 3D SAR)成像产生影响。该文建立了载机在飞行过程中存在偏航角速度下的DLSLA 3D SAR成像模型,通过理论推导得到了信号的多普勒调频率表达式,多普勒调频率与目标被调制后的跨航向坐标有关,而与被调制后的方位向坐标无关。进一步,完成跨航向信号处理之后,在平台的速度和偏航角速度不准的情况下,利用参数化稀疏表征方法实现了平台的速度和偏航角速度的估计,并完成了方位向稀疏场景的重构,最后提出了一种形变校正方法。仿真实验验证了该算法的有效性。

Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques

The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.

Inverse synthetic aperture radar imaging based on sparse signal processing

Based on the measurement model of inverse synthetic aperture radar (ISAR) within a small aspect sector,an imaging method was presented with the application of sparse signal processing.This method can form higher resolution inverse synthetic aperture radar images from compensating incomplete measured data,and improves the clarity of the images and makes the feature structure much more clear,which is helpful for target recognition.The simulation results indicate that this method can provide clear ISAR images with high contrast under complex motion case.

Two-stage DOA estimation method for passive radar based on sparse representation

In a GPS illuminator based passive radar system,estimation of direction of arriving(DOA) of multiple targets is a difficult problem due to strong interference.A two-stage method combining extensive cancellation algorithm(ECA) and sparse representation is proposed.In the first stage,ECA algorithm is used to eliminate the direct-path and multi-path interference.In the second stage,sparse representation of improved weight constraints based on L1 norm is adopted to estimate DOA and suppress the interference.Simulation results show that the proposed method can effectively estimate DOA in low computation complexity without estimating the disturbance parameter.

远距离高分辨激光成像雷达技术

星载和机载远距离目标的高分辨成像一直是科研人员不断追求的目标,为了突破远距离光学系统孔径衍射受限的问题,国内外先后发展了多种激光成像雷达技术。着重介绍了基于"点发射、面照射、点接收"工作模式来实现远距离目标高分辨成像的四种激光成像雷达基本原理,给出了这几种类型激光成像雷达的国内外实验和工程化进展程度,并进行了相应的比较。

Current progress in sparse signal processing applied to radar imaging

Sparse signal processing is a signal processing technique that takes advantage of signal’s sparsity,allowing signal to be recovered with a reduced number of samples.Compressive sensing,a new branch of the sparse signal processing,has become a rapidly growing research field.Sparse microwave imaging introduces the sparse signal processing theory to radar imaging to obtain new theories,new systems and new methodologies of microwave imaging.This paper first summarizes the latest application of sparse microwave imaging,including Synthetic Aperture Radar(SAR),tomographic SAR and inverse SAR.As sparse signal processing keeps evolving,an avalanche of results have been obtained.We also highlight its recent theoretical advances,including structured sparsity,off-grid,Bayesian approaches,and point out new research directions in sparse microwave imaging.