The Research Of Multiple Targets Localization Algorithm Based On Compressed Sensing And Particle Swarm Optimization Algorithm In Wireless Sensor Networks

With the commercialization of 5G technology around the world,as an important application scenario of 5G technology,the Internet of Things will develop rapidly.What's more,as the bottom layer network of the Internet of Things,Wireless Sensor Networks has also become a research hotspot as well.Thanks to most of applications in Wireless Sensor Networks are location-based services,it is meaningful to study the methods of positioning targets.At present,all traditional localization algorithms need to collect a large amount of data to estimate the position of targets,which conflicts with the characteristics of sensor such as limited energy,weak computing power,low storage,and low bandwidth.Therefore,in order to locate the targets with lower resource consumption,in recent years,researchers have applied compressed sensing theory into target positioning.In this paper,the main work is to solve the multiple targets localization problem based on compressed sensing under the indoor environment.The main work can be outlined as follow.(1)In order to improve the anti-noise capability of the positioning methods,we transform the multiple targets localization problem into the classic 0-1 knapsack problem.Then,the compressed sensing theory and swarm intelligence optimization algorithm are combined to locate multiple targets in the monitoring area.(2)Multiple Localization Counting Procedure(MLCP)is introduced to solve the combinatorial explosion problem.In order to reduce the size of the feasible domain,the key idea of MLCP is to reduce the dimension of the optimization problem by narrowing the search range of the optimization algorithm from the global area to some small regions.What's more,the effectiveness and reliability of MLCP method are verified by simulation experiments.(3)According to the prior information of our optimization problem and the mutation and crossover ideas of genetic algorithm,an Improved Binary Particle Swarm Optimization(IBPSO)algorithm is proposed,which is based on the standard Binary Particle Swarm Optimization(BPSO)algorithm.Then,Multiple Targets Localization Based on Improved Binary Particle Swarm Optimization(MTL-IBPSO)algorithm is proposed by combining compressed sensing theory and IBPSO algorithm.Finally,according to the simulation results,compared with other classic localization algorithms which are based on compressed sensing,the superiority of MTL-IBPSO algorithm is verified.(4)Based on MTL-IBPSO algorithm,Divided Localization of Multiple Targets(DLMT)algorithm is proposed.Compared with MTL-IBPSO algorithm,DLMT algorithm mainly adds three optimization strategies to further reduce the time consumption of positioning and improve the localization accuracy.In the end,compared with MTLIBPSO algorithm and other classic localization algorithms based on compressed sensing,the effectiveness of DLMT algorithm is verified through simulation.