Multiple Target Localization Algorithm Research Based On Compressive Sensing In Wireless Sensor Network

In recent years,the rapid development of Wireless Sensor Networks(WSNs)technology has brought vitality and vigor to many fields.As a key part of WSNs related technology,multiple target localization technology has been widely used in environmental detection,enemy tracking,and black box positioning of crashed aircraft.However,due to the limitations of hardware devices,traditional multiple target localization algorithms such as Time Difference of Arrival(TDOA),Time of Arrival(TOA),Angle of Arrival(AOA)are often restricted by cost and complexity,so that the application requirements cannot be achieved.Researchers are working on a multiple target localization algorithm with relatively high accuracy,low power consumption,and low complexity.Compressive sensing,a technology that can sample signals at a sampling frequency much lower than that required by the Nyquist sampling frequency,can restore sparse signals to the original signals through reconstruction algorithms.This technique is found to be used to solve the problem of multiple target localization with sparsity in the spatial domain.The application of compressive sensing technology can transfer the main computing tasks to the fusion center for execution,reducing the computing burden of sensor nodes.In this paper,combined with compressive sensing technology,the multiple target localization algorithm under the wireless sensor network is studied,and an improved localization algorithm is proposed.The research content of this article is mainly divided into the following two aspects:Firstly,the multiple target localization algorithm of Greedy Matching Pursuit(GMP)algorithm applied to multiple target localization scenarios is studied.The traditional algorithm has a high false alarm missed detection rate and the target is not in the center of the grid.For solving problems such as large errors,an improved GMP multi-objective algorithm is proposed,which significantly improves the false alarm and missed detection of positioning results.At the same time,multi-resolution algorithms are combined to further improve the positioning accuracy when the target position is random.The simulation results show that the improved algorithm proposed in this paper is better than the comparison algorithm in terms of positioning accuracy,false alarm probability and missed detection probability.Secondly,in view of the influence of the perceptual matrix preprocessing on the positioning performance in the compressive sensing process,the LU decomposition algorithm is studied.Simulations prove that the algorithm has higher positioning accuracy and positioning speed than the Orth algorithm.On this basis,this study compares the positioning performance of different reconstruction algorithms,analyzes used scene of different reconstruction algorithms,and proposes a method based on LU decomposition and Stagewise Orthogonal Matching Pursuit(St OMP).The target positioning algorithm is applied to the three-dimensional positioning model.Simulation analysis shows that the algorithm has better positioning accuracy than the comparison algorithm in three-dimensional space,and the positioning speed is significantly improved.