Spotlight SAR sparse sampling and imaging method based on compressive sensing

Spotlight synthetic aperture radar(SAR)emits a chirp signal and the echo bandwidth can be reduced through dechirp processing,where the A/D sampling rate decreases accordingly at the receiver.Compressive sensing allows the compressible signal to be reconstructed with a high probability using only a few samples by solving a linear program problem.This paper presents a novel signal sampling and imaging method for application to spotlight SAR based on compressive sensing.The signal is randomly sampled after dechirp processing to form a low-dimensional sample set,and the dechirp basis is imported to reconstruct the dechirp signal.Matching pursuit(MP)is used as a reconstruction algorithm.The reconstructed signal uses polar format algorithm(PFA)for imaging.Although our novel mechanism increases the system complexity to an extent,the data storage requirements can be compressed considerably.Several simulations verify the feasibility and accuracy of spotlight SAR signal processing via compressive sensing,and the method still obtains acceptable imaging results with 10%of the original echo data.     

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 multi-channel 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.     

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。     

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.     

基于压缩感知理论的单像素成像系统研究

近年来出现的压缩感知理论为信号处理的发展开辟了一条新的道路,它指出可压缩或者稀疏信号的少量线性投影含有足够的信息来进行信号重建和信号处理,在压缩感知理论的基础上,一种新的单像素成像系统的发展得到了广泛的关注,它的主要特点就是只用一个像素的探测器通过用少于图像像素值的采样数目来重建图像,主要介绍了基于压缩感知理论的单像素成像系统的图像重建算法,为单像素成像系统的发展做了有益的探索。     

基于压缩感知的多尺度绝缘子跟踪算法

针对多旋翼无人机(UAV)在电力巡检中的绝缘子跟踪问题,提出一种尺度自适应的绝缘子跟踪算法,采用稀疏投影的方式对原始图像特征进行降维,使用朴素贝叶斯分类器进行二分类,改进传统压缩感知(CS)跟踪搜索框固定问题,利用绝缘子的Lab空间特性进行分割,根据分割结果中绝缘子有效像素所占比例来改变搜索框的尺度,实现跟踪中的尺度自适应.实验结果表明:该算法能够在实验室和野外环境下自适应绝缘子尺度变化,对未来电力巡检智能化具有重大意义.     

压缩感知重构匹配类算法分析

压缩感知理论是一种利用信号的稀疏性或可压缩性而把采样与压缩融为一体的新理论体系,它成功地克服了传统理论中采样数据量大、资源浪费严重等问题。该理论的研究方向主要包括信号的稀疏表示、测量矩阵的设计和信号的重构算法。其中信号的重构算法是该理论中的关键部分,也是近年来研究的热点。本文主要对匹配追踪类重构算法作了详细介绍,并通过仿真实验结果对这些算法进行了对比和分析。     

基于Bayesian压缩感知的融合算法

根据压缩感知理论中的采样模式,提出了一种基于改进采样模式的压缩域图像融合算法.该算法首先通过双星型采样模式获得待融合图像的稀疏域压缩测量值,然后利用一种简单的绝对值最大融合规则直接在压缩感知域进行融合,最后通过贝叶斯的方法重构融合图像.在图像重建的过程中采用了贝叶斯方法.由于考虑了误差以及噪声的影响,使得融合图像的质量进一步提高.仿真结果表明,该算法具有良好的融合效果.     

基于压缩感知的实时手势检测和跟踪算法

基于计算机视觉的实时手势检测与跟踪算法是人机交互领域的一项关键技术,传统的手势检测与跟踪算法将检测和跟踪分成两个独立的模块进行,检测与跟踪结果受手势姿态变化、目标遮挡、运动模糊以及外界环境干扰等因素的影响。提出了一种基于压缩感知的实时手势检测和跟踪算法,将基于检测得到的手势信息与基于压缩感知跟踪算法得到的目标信息进行有效融合,从而实现有效的手势检测与跟踪,与传统算法相比,该算法能实现手势跟踪自动初始化和跟踪错误后自我恢复功能。实验结果表明,提出的算法能对手势运动进行快速、连续、准确的识别,满足人机交互的要求。     

基于压缩传感的手写字符识别方法

基于新出现的压缩传感理论,提出了一种鲁棒的手写字符识别方法,能很好地对含有噪声的字符进行识别.该方法通过对测试字符进行稀疏表示,采用l1范数最小化算法求得最稀疏的系数解,所获得的系数具有明显的类别信息,从而易于对测试字符进行分类.实验结果表明,该方法具有很好的噪声鲁棒性.     

基于小波域压缩感知的遥感图像超分辨算法

针对单字典表达复杂多样的图像纹理存在一定的局限性的问题,利用压缩感知和小波理论建立了一种多字典遥感图像超分辨算法。首先,对训练图像在小波域的不同频带利用K-奇异值分解（K-SVD）算法建立不同的字典;然后,利用全局限制求取高分辨率图像的初始解;最后,利用正交匹配追踪算法（OMP）对初始解在小波域进行多字典稀疏求解。实验结果表明,相比基于单字典的超分辨重建算法,结果图像的主观视觉效果有很大提高,客观评价指标的峰值信噪比（PSNR）和结构相似度（SSIM）分别提高2.8 dB以上和0.01以上。字典可一次建立重复使用,降低了运算时间。     

Suppressing azimuth ambiguity in spaceborne SAR images based on compressed sensing

In spaceborne synthetic aperture radar,undersampling at the rate of the pulse repetition frequency causes azimuth ambiguity,which induces ghost into the images.This paper introduces compressed sensing for azimuth ambiguity suppression and presents two novel methods from the perspectives of system design and image formation,known as azimuth random sampling and ambiguity separation,respectively.The first method makes the imaging results for the ambiguity zones as disperse as possible while ensuring that the imaging results for the main scene are affected as little as possible.The second method separates the ambiguity signals from the echoes and achieves imaging results without the ambiguity effect.Simulation results show that the two methods can reduce the ambiguity levels by about 16 dB and 99.37%,respectively.     

基于压缩感知的拥塞控制机制

针对无线传感网络(WSN)的拥塞问题,提出一种压缩感知与速率控制相结合的拥塞缓解方法。针对局部拥塞,通过开环逐条反压机制调整上游节点的发送速率,从而快速缓解局部拥塞;针对全局拥塞,各节点采用压缩感知的方法采集数据,减小采集信息的冗余,从而缓解全局性拥塞。拥塞缓解后,节点正常采样,以此来保证数据的真实度。仿真结果表明,该方法对WSN的拥塞具有较好的控制效果。     

一种基于压缩感知的邻域优化算法

非线性降维方法是目前对降维研究有着重要影响的方法,但在降维过程中经常会遇到局部邻域信息量不足、短路和噪声干扰等问题,严重影响降维效果,很难广泛应用于真实数据的处理中.对以上问题分析发现,其主要原因在于经典降维算法都是采用全局固定的邻域大小.提出了一种基于压缩感知的邻域优化算法,运用压缩感知技术对高维空间目标点近邻进行压缩采样,构建“收—放”模型,自适应得到最优子空间,同时优化邻域组成元素,使得数据的整体降维效果更加稳定.通过手工流形和真实数据集的实验,验证了算法的有效性和稳定性.     

基于压缩感知的正交频分复用信道估计方法

传统的信道估计方法未充分利用信道的稀疏性。利用信道冲激响应的稀疏特性,提出了将FOCUSS(Focal Underdetermined System Solver)压缩感知算法应用到信道估计中,从均方误差和正确检测率方面分别与传统最小二乘估计和正交匹配追踪压缩感知信道估计相比较。仿真结果表明,本文算法的均方误差较小,正确检测率较高,能够以较少的导频信号获得好的估计性能。     

基于改进压缩感知的运动目标跟踪

针对运动目标在被遮挡和目标纹理变化大时会导致跟踪丢失以及跟踪误差大等问题,提出了一种改进的压缩感知（ CS）算法。算法采用设置Sigmoid函数响应阈值,判定是否存在遮挡,以决定是否更新分类器参数,使得目标在遇到较大遮挡时目标模型不会被错误更新;针对特征单一导致跟踪不稳定问题,提出根据设定融合规则进行灰度特征和纹理特征融合的方法,使得两种特征指导跟踪。实验证明：改进后的算法比传统算法跟踪成功率提高了17.84%,平均误差率降低11.59%。