Research On Wireless Sensor Network Localization Algorithm Based On Compressed Sensing

With the rapid development of wireless sensor network technology,node location technology is an important part of wireless sensor network.At present,there are some disadvantages such as low energy and insufficient computing power,which limit the adaptive scenarios of wireless sensor network positioning technology.Therefore,to reduce the computational complexity as much as possible and design a precise and fast positioning algorithm is the most important task for wireless sensor networks.At present,compressed sensing technology can reduce the signal sampling rate and reduce the computing burden of nodes.Therefore,this paper combines compressed sensing technology to realize node location in wireless sensor network.The work contents of this paper mainly include:(1)Improved greedy matching tracking reconstruction algorithmFirstly,several commonly used compressed sensing reconstruction algorithms are analyzed and studied.On this basis,an improved algorithm based on SP algorithm--Sparsity Weak selection Adaptive Matching Pursuit(SPWAMP)algorithm is proposed.Firstly,the weak selection strategy is used to select the atoms that match the original signal more flexibly.Secondly,the threshold control method and variable step size method are used to control the convergence condition of the algorithm and adjust the estimation sparsity,so that the improved algorithm can get rid of the dependence on the signal sparsity.(2)Wireless sensor network localization algorithm based on compressed sensingThe improved compressed sensing reconstruction algorithm is combined with the positioning method.The received signal intensity was used to construct the compressed sensing observation matrix and observation value,and the improved SPWAMP algorithm was used to reconstruct the signal,determine the grid where the target was located,calculate the specific position of the node using the three-side measurement method,and realize the multi-target node positioning.Finally,MATLAB is used to simulate the improved reconstruction algorithm.By comparing and analyzing the reconstruction probability and running time of the improved algorithm and the traditional algorithm,it is concluded that the improved algorithm in this paper improves the probability of successful reconstruction on the premise of guaranteeing the reconstruction speed.Furthermore,the improved reconstruction algorithm is further combined with the positioning algorithm,so that the positioning algorithm can simultaneously obtain the location of all unknown nodes.Even if the unknown node is not in the grid center,the node positioning can be achieved with high positioning accuracy,avoiding locating only the unknown nodes in the grid center.