A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.

Wavelet Transform-Based Distributed Compressed Sensing in Wireless Sensor Networks

Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.

Sparse channel estimation for MIMO-OFDM systems using distributed compressed sensing

A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.

On the Energy Self-Sustainability of IoT via Distributed Compressed Sensing

This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

A new backtracking-based sparsity adaptive algorithm for distributed compressed sensing

A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing(DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but the numbers of non-zero(significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing strong DCS algorithms.

A DISTRIBUTED COMPRESSED SENSING APPROACH FOR SPEECH SIGNAL DENOISING

Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed sensing theory is the sparsity of signals.In this paper,two methods to estimate the sparsity level of the signal are formulated.And then an approach to estimate the sparsity level directly from the noisy signal is presented.Moreover,a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit(CoSaMP) algorithm.Several simulation results demonstrate the accuracy of the estimated sparsity level and that this de-noising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.

Design of the Quantization Matrix in the Distributed Compressed Sensing Video Coding*

In the frame of compressed sensing distributed video coding, the design of the quantization matrix directly affects the reconstruction quality of the receiving terminal of the video. In this article, we present a new design method of the Gaussian quantization matrix adapting to the compressed sensing coding, for that the distribution of the parameters of the image is featured of the characteristic of approximately normal distribution after measured by compressive sensing. By this way, the parameters of a certain quantity of the image frames depending on the video sequences generated by the Gaussian quantization matrix possess certain adaptive capacity. By comparison with the plan of the traditional quantization, the quantization matrix presented in this article would improve the reconstruction quality of the video.

Deformation and failure characteristics of sandstone under uniaxial compression using distributed fiber optic strain sensing

This paper investigates the deformation and fracture propagation of sandstone specimen under uniaxial compression using the distributed fiber optic strain sensing(DFOSS)technology.It shows that the DFOSS-based circumferential strains are in agreement with the data monitored with the traditional strain gage.The DFOSS successfully scans the full-field view of axial and circumferential strains on the specimen surface.The spatiotemporal strain measurement based on DFOSS manifests crack closure and elastoplastic deformation,detects initialization of microcrack nucleation,and identifies strain localization within the specimen.The DFOSS well observes the effects of rock heterogeneity on rock deformation.The advantage of DFOSS-based strain acquisition includes the high spatiotemporal resolution of signals and the ability of full-surface strain scanning.The introduction to the DFOSS technology yields a better understanding of the rock damage process under uniaxial compression.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

Research on Data Compression of WSN Based on Compressed Sensing

For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.

Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.

A Novel Training Sequence Applied to DCS-Based Channel Estimation

Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a cyclic prefix and reference signal. However, the DCS-based channel estimation requires diversity sequences instead of UW. In this paper, we proposed a novel method that employs a training sequence(TS) whose duration time is slightly longer than the maximum delay spread time. Based on proposed TS, the DCS approach perform perfectly in multipath channel estimation. Meanwhile, a cyclic prefix construct could be formed, which reduces the complexity of the frequency domain equalization(FDE) directly. Simulation results demonstrate that, by using the method of simultaneous orthogonal matching pursuit(SOMP), the required channel overhead has been reduced thanks to the proposed TS.

基于DCS的矿山物联网微震数据重构算法研究

煤矿物联网是近几年兴起的热点研究领域。针对煤矿物联网分布式环境下微震数据量大的问题,引入分布式压缩感知理论对微震数据进行压缩以减小数据传输量。以分布式微震信号为对象,通过傅里叶变换基对其进行稀疏性分析,论证了可以用压缩感知相关理论对微震数据进行压缩处理。基于广义正交匹配追踪算法及稀疏度自适应匹配追踪算法,提出了一种改进的分布式稀疏度自适应正交匹配追踪重构算法。基于MATLAB仿真平台,用改进的算法重构稀疏测量后的分布式微震信号,仿真结果表明,该算法在减少计算量的前提下有效地恢复了原始微震信号。

基于DCS的发射分集MIMO雷达参数估计

针对目标在多角度观测下的散射系数估计问题,研究了基于分布式压缩感知(distributed com-pressed sensing,DCS)的发射分集多输入多输出(multiple-input multiple-output,MIMO)雷达参数估计方法。在分析发射分集MIMO雷达信号模型的基础上,构建了其联合稀疏表示模型;在分析正交匹配追踪(orthogonalmatching pursuit,OMP)算法实现结构的基础上,提出了一种新的基于迭代式正交匹配追踪的DCS算法。仿真结果表明该方法的估计精度高于DCS-SOMP和幅度相位估计+Capon的算法,重构概率也高于DCS-SOMP算法。

一种改进的基于DCS的分布式多用户协作频谱感知方法

分布式压缩感知(DCS)理论扩展了压缩感知理论的应用,将单信号的压缩采样扩展到信号群的压缩采样。协作频谱感知技术利用空间的宏集合弥补了单用户在认知无线电宽带频谱感知过程中可能出现检测错误的问题,但在复杂的信号重构过程中增加了计算量。针对这种情况,提出了一种改进的基于DCS的分布式多用户协作频谱感知方法。该改进方法的重构过程是在原OMP算法的基础上,通过利用上一时刻频谱感知所得到的频谱占用情况减少重构算法的计算量。仿真结果表明,在频谱占用情况变化缓慢的情况下,所提的改进方法不仅具有与原算法相同的重构效果,而且在认知用户数量较多的情况下,重构复杂度明显减小。

基于地理相关性的电力物联网数据DCS算法研究与应用

压缩感知是近几年来发展起来的理论,首先对压缩感知理论做了简单的介绍,重点阐述了WSN中基于地理相关性分布式压缩感知理论。在电力信息采集系统中,信息量比较大,所需传感器节点数目繁多,数据的传输量也很大,对数据进行压缩是减少数据传输量的有效途径,根据实际应用情况,选用了基于地理相关性分布式压缩感知理论对网络层数据进行压缩,并取得了理想的实验结果。

基于DCS的WMSN多视角视频编解码

为了有效解决无线多媒体传感器网络中多视角视频监控传输数据量大以及网络能量、资源受限的问题,提出了一种基于分布式压缩感知的高压缩率多视角视频编解码方法。对多视角视频序列进行分组处理,并将图像组分为关键帧和非关键帧;对关键帧采用基于压缩感知(compressed sensing,CS)的编解码方法进行处理;而在非关键帧的编码端采用联合稀疏表示方法对残差图像稀疏表示,解码端利用帧间时间相关性和多视角空间相关性预测生成当前视频帧,并借助差异补偿方法进一步提高预测准确性,同时提高了重构效果。实验结果表明,该方法取得较高的压缩率,重构出的图像质量比参考方法更高,且PSNR值得到了较大的提高。

基于DCS的统计MIMO雷达信号模型及参数估计

分布式压缩感知(Distributed Compressed Sensing,DCS)将单信号的压缩采样扩展到信号群的压缩采样,利用信号内相关性和互相关性对多个信号进行联合重构。统计多输入多输出(Multiple-Input Multiple-Output,MIMO)雷达系统通过多发多收配置,在发射机、目标以及接收机之间构成对目标的分布式探测系统。该文将DCS应用到统计MIMO雷达中,通过对该场景中目标回波的延时在距离空间稀疏性的分析,提出联合所有接收信号重构目标场景的设想,建立了接收信号的联合稀疏模型,并实现了目标参数估计的联合重构算法。仿真结果表明与基于压缩感知(Compressed Sensing,CS)的算法相比,基于DCS的算法在进一步降低采样数目的同时提高了参数估计精度,同时也验证了DCS-MIMO雷达可以有效克服目标的雷达散射截面积(Radar Cross Section,RCS)起伏。

无线传感器网络中DCS算法的研究与应用

分布式无线传感网络面临着数据采样和传输方面的发展瓶颈问题。本文研究分析了能较好地适应并解决这一问题的分布式压缩感知(Distributed Compressed Sensing,DCS)算法。从理论上重点研究了DCS算法中JSM(Joint Sparse Model,JSM)的三种模型JSM-1,JSM-2,JSM-3。通过仿真对CS和DCS算法性能进行了对比分析。结果表明:DCS算法相比CS算法能以更少的测量值实现对原始信号群的精确重建,降低了节点能耗,延长了网络的生命周期。