Localization Algorithm Research Based On Compressive Sensing In Wireless Sensor Network

With the development of the wireless communication,digital electronic technology and micro-electromechanical technology,wireless sensor network(WSN)has been rapid development and extensive application.Localization is the most essential problem since the location information is the important premise for many applications and services.As WSN is a kind of low-power and low-cost network,localization technology must have the advantages of low-cost,low-power,low-complexity and high-accuracy.However,the existing localization algorithms have limitations on adaptability.Therefore,it has theoretical significance and applicable value to improve the adaptability of localization algorithms.This paper mainly has studied localization algorithm in wireless sensor network.To solve localization problems under the different network conditions,the paper offers a few methods for these problems in wireless sensor network by using compressive sensing,which is the frontier theory in signal processing field.Specifically,the paper's main research results and innovation points include the following aspects:Firstly,node localization under static beaconsThere exist many node localization algorithms under static beacons,but most algorithms are only suitable for some specific applications and they are not very easy to achieve under many environments.Therefore,to let localization be reliability,availability and usability,an algorithm needs to meet some suitable requirements while the algorithm solves the localization problems.Then,according to these requirements,a novel algorithm-node localization algorithm based on compressed sensing(NLCS)is proposed.The proposed algorithm combines compressive sensing with centroid to estimate the location of sensor node by fully exploring the spatial correlation between the sensor nodes,so the proposed algorithm has good localization performance.In addition,since the proposed algorithm can meet these requirements,it has good adaptability.Since the connectivity information is collected by minimum hop between nodes,and the minimum hop is integer,the information describes the spatial relationship between the nodes approximately.Therefore,in order to improve the localization performance,this paper presents the concept of pseudo-hop,which can describe the spatial relationships between nodes accurately.An improved NLVS(INLCS)is proposed by using pseudo-hop.The proposed algorithm not only inherits the advantages of NLCS,but also greatly improves the localization performance.Secondly,node localization under mobile beaconSince a sensor node can senses some mobile beacon points,and it is sole in its sensing area,we propose a novel localization algorithm——node localization algorithm via sparse(NLVS).Firstly,the proposed algorithm divides the sensing area into grids,and converts the node localization problem to a reconstruction problem.And then,the proposed algorithm adopts weighted centroid to solve the problem that the signal is an approximate signal.In addition,the measurement matrix does not meet restricted isometry property(RIP)in NLVS.To solve the problem,this paper proposes a novel localization algorithm——sparse localization algorithm with a mobile beacon based on LU-decomposition(SLMLU).The proposed algorithm adapts LU-decomposition to solve the problem that the measurement matrix does not meet RIP.Therefore,the localization performance can be further improved.Finally,target localizationThe paper firstly analyzes the strengths and weaknesses of sparse target localization based on orth-processing,and then sparse target localization based on QR-decomposition is proposed.The proposed algorithm can effectively ensure that the measurement matrix meets RIP,and QR-decomposition can not affect the sparsity of the original signal.Therefore,the proposed algorithm has a better target localization accuracy.