Compressive sensing based data gathering algorithm over unreliable links in WSN

To solve the problem that the ubiquitous unreliable links in the WSN influence the performance of the compressive sensing (CS) based data gathering,first the relationship between the reconstruction SNR of CS-based data gathering algorithm and the bit-error-ratio (BER) were simulated quantitatively.Then classify two cases were classified,namely light-payload and heavy-payload,relying on the analysis of wireless link packet loss characteristics.The random packet loss model was conceived to describe the packet loss under light-payload scenario.Further the neighbor topology spatial correlation prediction-based CS data gathering (CS-NTSC) algorithm was proposed,which utilized the nodes spatial correlation to reduce the impact of error.Additionally,the node pseudo-failure model was conceived to describe the packet loss occurred in network congestion,and then the sparse schedule-aided CS data gathering (CS-SSDG) algorithm were conceived,for the purpose of changing the sparsity of measurement matrix and avoiding measurements amongst the nodes affected by unreliable links,thus weakening the impact of error/loss on data reconstruction.Simulation analysis indicates that the proposed algorithms are not only capable of improving the accuracy of the data reconstruction without extra energy,but also effectively reducing the impact affected by the unreliable links imposed on CS-based data gathering.     

传感网中基于压缩感知的丢包匹配数据收集算法

为了提高传感网中数据重构精度以及降低不可靠链路丢包对压缩感知（Compressive Sensing，CS） 数据收集的影响，本文提出了一种基于压缩感知丢包匹配数据收集算法（Packet Loss Matching Data Gathering Algorithm Based on Compressive Sensing，CS-MDGA）.本文算法通过压缩感知技术构建了全网数据间的"关联效应"，并设计了基于丢包匹配的稀疏观测矩阵（Sparse Observation Matrix Based on Packet Loss Matching，SPLM），证明了该观测矩阵概率趋近于"1"时，满足的等距约束条件（Restricted Isometry Property，RIP），完成了节点间多路径路由数据的可靠交付.仿真实验结果表明，本文算法在链路丢包率为60%情况下，相对重构误差仍小于5%，验证了本文算法不仅具有较高的重构精度，而且还可以有效缓解不可靠链路丢包对CS数据收集的影响.     

Sparse signal transmission under lossy wireless links based on double process of compressive sensing

In resource-limited wireless sensor networks,links with poor quality hinder its large-scale applications seriously.Thanks to the inherent sparse property of signals in WSN,the framework of sparse signal transmission based on double process of compressive sensing was proposed,providing an insight into a new way of real-time,accurate and energy-efficient sparse signal transmission.Firstly,the random packet loss during transmission under lossy wireless links was modeled as a linear dimension-reduced measurement process of CS (a passive process of CS).Then,considering that a large packet was often adopted in WSN for higher transmission efficiency,a random linear dimension-reduced projection (a simple source coding operation) was employed at the sender node (an active process of CS) to prevent block data loss.Now,the raw signal could be recovered from the lossy data at the receiver node using CS reconstruction algorithms.Furtherly,according to the theory of CS reconstruction and the formula of packet reception rate in wireless communication,the minimum compression ratio and the maximum packet length allowed were obtained.Extensive simulations demonstrate that the reliability of data transmission and its accuracy,the data transmission volume,the transmission delay and energy consumption could be greatly optimized by means of proposed method.     

Compressed Blind Signal Reconstruction Model and Algorithm

The model of inherent connection between underdetermined blind signal separation and compressed sensing (CS) is analyzed first; then, the mathematical model of underdetermined blind signal reconstruction is built using CS. More specifically, the mixing matrix is estimated by exploiting the wavelet packet transform and k-means clustering methods up to permutation and scaling indeterminacy, and then, the measurement matrix and the measurement equation are obtained. To reconstruct the underdetermined sparse source signals, the proposed semi-blind compressed reconstruction algorithm is derived based on the blind signal reconstruction model and compressive sampling matching pursuit (CoSaMP) method. Our simulation results demonstrate that the proposed scheme is effective, irrespective of artificial data or real data. Moreover, the proposed scheme can be adjusted for different applications by modifying the mixing matrix estimation method and CoSaMP method with respect to the correspondence conditions.     

Denoising recovery for compressive sensing based on selective measure

In order to reduce the effect of noise folding (NF) phenomenon on the performance of sparse signal recon-struction,a new denoising recovery algorithm based on selective measure was proposed.Firstly,the NF phenomenon in compressive sensing (CS) was explained in theory.Secondly,a new statistic based on compressive measurement data was proposed,and its probability density function (PDF) was deduced and analyzed.Then a noise filter matrix was constructed based on the PDF to guide the optimization of measurement matrix.The optimized measurement matrix can selectively sense the sparse signal and suppress the noise to improve the SNR of the measurement data,resulting in the improvement of sparse reconstruction performance.Finally,it was pointed out that increasing the measurement times can further enhance the performance of denoising reconstruction.Simulation results show that the proposed denoising recon-struction algorithm has a better improvement in the performance of reconstruction of noisy signal,especially under low SNR.     

基于随机滤波的探地雷达成像方法

探地雷达是一种超宽带雷达系统,若按传统的奈奎斯特采样,雷达回波信号需要大量空间存储。压缩感知可以实现利用少量的测量值对稀疏信号进行重构,其中最为关键的是测量矩阵和重构算法的选择。本文将压缩感知应用于探地雷达成像,并利用随机滤波的思想选择测量矩阵,可以有效减少测量矩阵中非零值的个数。利用正交匹配追踪算法对信号进行重构,算法简单,降低了数据的存储量和运算复杂度,该算法同样可以对时间和空间上同时压缩的数据进行成像。最后,本文给出基于时间连续信号的GPR接收机一种CS实现方案。仿真结果表明,本文提出的成像方法可以以少量数据精确地对信号进行重构,并且运算量少。      

UWSNs中面向能耗和延时优化的移动数据收集

为了降低水下无线传感网(UWSN)中数据收集的能耗和保证实时性,提出一种基于压缩感知的移动数据收集方案。以分布式能量均衡非均匀分簇(DEBUC)协议和压缩感知理论为基础,簇内节点依据设计的稀疏测量矩阵决定是否参与压缩采样,并将获得的测量值传输至簇头。然后,通过自主式水下潜器(AUV)的移动来收集各个簇头上的数据到数据中心,该问题被建模为基于信息质量最大化的旅行商问题(TSP),并提出近似算法进行求解。仿真实验结果表明,相比于已有的水下移动数据收集算法,本文方案在保证数据收集可靠性的同时,缩短了数据收集延时,延长了网络寿命。     

运动补偿预测残差稀疏重构的分布式压缩视频传感

针对低复杂度视频编码需求,基于压缩传感(CS：Compressive Sensing)理论,提出了一种分布式压缩视频传感算法。低复杂度的编码器独立随机投影关键帧和CS帧,采集压缩视频数据;在解码端进行运动补偿预测以利用帧间相关性,对预测残差稀疏重构实现CS帧重建。仿真测试表明,与现有的三种压缩视频传感算法相比,所提算法重建的视频质量更好,适合无线视频监控及无线视频传感网络等应用。     

认知无线电NC-OFDM系统中基于压缩感知的信道估计新方法

提出了一种基于压缩感知(CS,compressive sensing)理论的不连续子载波正交频分复用(NC-OFDM,non-contiguous orthogonal frequency division multiplexing)系统信道估计新方法,全面研究了认知无线电NC-OFDM系统CS信道估计的理论框架、导频图案的设计、信道估计算法,并依据CS测量矩阵设计理论提出了测量矩阵互相关最小化的导频图案优化准则。仿真结果表明,同已有的NC-OFDM系统信道估计方法相比,CS信道估计能够在多种禁用子载波场景下,使用较少导频获得很好的信道估计性能。     

基于概率稀疏随机矩阵的压缩数据收集方法

测量矩阵设计是应用压缩感知理论解决实际问题的关键。该文针对无线传感器网络压缩数据收集问题设计了一种概率稀疏随机矩阵。该矩阵可在减少参与投影值计算节点个数的同时,让参与投影值计算的节点分布集中化,从而降低数据收集的通信能耗。在此基础上,为提高网络数据重构精度,又提出一种适用于概率稀疏随机矩阵优化的测量矩阵优化算法。仿真实验结果表明,与稀疏随机矩阵和稀疏Toeplitz测量矩阵相比,采用优化的概率稀疏随机矩阵作为压缩数据收集的测量矩阵可显著降低通信能耗,且重构误差更小。     

基于LANDMARC与压缩感知的双段式室内定位算法

鉴于已有室内定位算法定位精度与运算效率之间的矛盾,该文提出一种将LANDMARC区域定位与基于模拟退火优化正则化正交匹配追踪(SROMP)的压缩感知位置估计相结合的双段式定位算法(LANDMARC- SROMP CS)。首先,利用LANDMARC定位算法快速锁定目标所在区域范围;在锁定的区域内,再引入压缩感知理论实现目标位置估计。此部分,首先根据锁定区域范围建立虚拟参考标签;然后由新型组合核函数相关向量机算法训练得到室内传播损耗模型,计算获得虚拟标签处接收信号强度值,构建测量矩阵;最后利用SROMP压缩感知重构算法求解出目标的位置索引矩阵,对索引矩阵中的位置相关点加权平均得到目标的位置信息。实验结果表明,所提定位算法平均定位误差为0.6445 m,算法运算效率相对较高,可以较好地满足室内定位的要求。     

压缩感知理论及其应用前景

压缩感知理论是近年来提出的一种基于信号稀疏性的新兴采样理论。与通常的数据采样定理不同,该理论提出可以用远远少于传统采样定理所需的采样点数或观测点数恢复出原信号或图像。本文主要阐述了压缩感知中信号的稀疏表示、测量矩阵的设计及信号的重构算法等基本理论,论述了该理论的广阔应用前景。     

运动补偿预测残差稀疏重构的压缩视频传感

针对低复杂度视频编码需求,基于压缩传感(Compressive Sensing,CS)理论,提出了一种分布式压缩视频传感算法。低复杂度的编码器独立随机投影关键帧和CS帧,采集压缩视频数据;在解码端进行运动补偿预测以利用帧间相关性,对预测残差稀疏重构实现CS帧重建。仿真测试表明,与现有的3种压缩视频传感算法相比,所提算法重建的视频质量更好,适合无线视频监控及无线视频传感网络等应用。     

基于信息辅助块稀疏贝叶斯学习的直扩通信窄带干扰抑制

现有基于Nyquist-Shannon采样定理的窄带干扰(Narrowband Interference,NBI)抑制方法存在应用受限于采样率较高的问题。应用压缩感知(Compressive Sensing,CS)理论解决上述问题,利用NBI在频域表现出的块稀疏特性以及直接序列扩频(Direct Sequence Spread Spectrum,DSSS)信号的类噪声特性,提出了基于块稀疏贝叶斯学习(Block Sparse Bayesian Learning,BSBL)框架的DSSS通信NBI抑制模型。实现干扰抑制后,利用传统的CS重构算法实现DSSS信号的压缩域解调。为进一步提高算法性能,将NBI稀疏分块的块内自相关矩阵建模为单位矩阵,提出了信息辅助BSBL(Aid BSBL,ABSBL)算法,设计了基于ABSBL的DSSS通信NBI抑制算法。该算法在保持较好NBI抑制性能的条件下,提高了运算效率并且不依赖NBI的稀疏结构。仿真验证和对比分析结果表明,所提方法能够有效抑制DSSS通信中的NBI,在干扰强度相同的条件下,NBI带宽越小、压缩率越大,算法对NBI的抑制性能越好。     

一种基于El Gamal加密算法的压缩数据收集方法

压缩感知技术在信号处理、图像处理、数据收集与分析等方面有很大优势,是近年来的研究热点。研究了如何安全高效地运用压缩感知技术来收集无线传感器网络中的数据。传统的基于压缩感知技术的数据收集方法并不考虑数据收集的安全性,而且网络内的所有节点都会参与每个测量值的收集。将El Gamal加密算法和基于稀疏随机矩阵的压缩感知技术相结合,提出了一种基于El Gamal加密算法的稀疏压缩数据收集方法（El Gamal based sparse compressive data gathering,ESCDG）。理论分析和数值实验表明,ESCDG不仅能降低网络资源的消耗而且能抵御多项式算力的内部攻击和外部攻击。     

Interference suppression for ultra dense network based on compressive sensing framework

Ultra-dense network (UDN) deployment of small cells introduces novel technical challenges, one of which is that the interference levels increase considerably with the network density. This paper proposes interference suppression scheme based on compressive sensing (CS) framework for UDN. Firstly, the measurement matrix is designed by exploiting the sparsity of millimeter wave channels. CS technique is employed to transform the high dimension sparse signal into low dimension signal. Then, the interference is canceled in the compressed domain. Finally, the stagewise weak orthogonal matching pursuit (SWOMP) algorithm is used to reconstruct the useful signal after interference suppression. The analysis and simulation results demonstrate the effectiveness of the algorithm. Simulation results demonstrate that the proposed interference suppression in compressive domain yields performance gains compared to other classical interference suppression schemes. The proposed algorithm can reduce the computational complexity of interference suppression algorithm.     

基于分块压缩感知的图像全局重构模型

已有的基于分块压缩感知（Block Compressed Sensing,Block CS）的图像重构模型采用相同的测量矩阵以块×块的方式获取数据,解决了传统CS方法中测量矩阵所需存储量较大的问题,但由于采用分块重构,没有考虑到图像的全局稀疏度,出现了大量的块效应。本文分析了图像分块重构产生块效应的三个主要原因:块稀疏度不均匀、频谱泄漏和块尺寸受限,提出了一种基于Block CS的图像全局重构模型。该模型在编码端采用高斯随机矩阵逐块作非相关测量;在解码端,引入排序算子,重新构造测量矩阵,该测量矩阵既适合于进行全局重构,又适合于分块测量的CS观测值,并仍与图像的稀疏矩阵高度不相关,所以其可充分利用图像的全局稀疏度进行CS重构。仿真实验表明,所提出的全局重构模型有效地消除了块效应现象,并且对块尺寸的变化有较强的鲁棒性。      

一种基于多维交替方向乘子法的多输入多输出逆合成孔径雷达成像方法

基于傅里叶变换的传统逆合成孔径雷达(ISAR)成像方法存在数据存储量大、数据采集时间长的问题.压缩感知(CS)理论利用图像的稀疏性,可以利用有限的数据恢复图像,这极大降低了数据采集成本.但对于多维数据,传统压缩感知方法要将多维数据转化成一维向量,这造成了很大存储和计算负担.因此,该文提出一种基于多维度-交替方向乘子法(...     

Image representation using block compressive sensing for compression applications

The emerging compressive sensing (CS) theory has pointed us a promising way of developing novel efficient data compression techniques, although it is proposed with original intention to achieve dimension-reduced sampling for saving data sampling cost. However, the non-adaptive projection representation for the natural images by conventional CS (CCS) framework may lead to an inefficient compression performance when comparing to the classical image compression standards such as JPEG and JPEG 2000. In this paper, two simple methods are investigated for the block CS (BCS) with discrete cosine transform (DCT) based image representation for compression applications. One is called coefficient random permutation (CRP), and the other is termed adaptive sampling (AS). The CRP method can be effective in balancing the sparsity of sampled vectors in DCT domain of image, and then in improving the CS sampling efficiency. The AS is achieved by designing an adaptive measurement matrix used in CS based on the energy distribution characteristics of image in DCT domain, which has a good effect in enhancing the CS performance. Experimental results demonstrate that our proposed methods are efficacious in reducing the dimension of the BCS-based image representation and/or improving the recovered image quality. The proposed BCS based image representation scheme could be an efficient alternative for applications of encrypted image compression and/or robust image compression.     

基于块稀疏贝叶斯学习的雷达目标压缩感知(英文)

高速采样和传输是目前雷达系统面临的一个重要挑战。针对这一问题,该文提出一种利用信号块结构特性的雷达目标压缩感知方法。该方法采用一个简单的测量矩阵对信号进行采样,然后运用块稀疏贝叶斯学习算法恢复信号。经典的块稀疏贝叶斯学习算法适用于实信号,该文将其扩为可直接处理雷达信号的复数域稀疏贝叶斯算法。相对于现有压缩感知方法,该方法不仅具有更好的信号重构精度和鲁棒性,更重要的是其压缩测量矩阵形式简单、易于硬件实现。数值仿真实验结果验证了该方法的有效性。