Research On WSN Node Location Optimization Algorithm Based On Particle Filter Framework

Wireless Sensor Network(WSN)is one of the most popular technologies studied in the past two decades.Its own advantage is that,compared with other technologies,WSN can perform monitoring tasks autonomously for a long period of time with low cost.Because of the variety of physical measurement types that its network can collect and process,WSN are used in many situations: military surveillance,infrastructure security,environmental and habitat monitoring,industrial sensing and traffic control.Positioning technology is an important supporting technology in these applications.Among them,based on the geographical information of the sensor nodes to ensure the location of the tracking object,it can provide help in areas that people need and supply.In large WSN applications,nodes are usually randomly deployed in a wide area,and most of the locations are unknown.However,the application of a large number of WSN requires the location of these nodes and the location of the information source for obtaining the location information of these network events.The Particle Filter(PF)-based WSN positioning algorithm transforms the positioning problem into a state estimation problem and uses Bayesian estimation to deal with the positioning problem.Comparing with the algorithms based on ranging and non-ranging positioning,the filtering-based algorithm has the advantages of high stability and high positioning accuracy.The PF takes full advantage of the observation information in the WSN to make an estimate of the node status at the current moment,so it has high stability and high positioning accuracy.For this reason,adopting PF to implement the WSN positioning method has always been a research hotspot since it was proposed.The positioning technique in WSN is a nonlinear and non-Gaussian filtering problem,and PF is widely used to cope with non-linear Gaussian wireless network positioning problems.It generates Monte Carlo methods for node particles with a certain probability density distribution of these known states.The node particle posterior probability is solved to obtain the optimal location information of the target node state.However,using the PF algorithm in the WSN has traditionally been a difficult problem because its inherent particle degradation and depletion,which affect the accuracy of the target node location estimation.In order to solve this problem,this paper proposed using two strategies: quality prediction and centroid drift to choose the high quality prevents particles from degrading and makes their distribution more reasonable.Through simulation and analysis,the algorithm proposed in this paper is robust and relatively low in computational complexity.This method of state estimation is suitable for applications in wireless positioning.The main research work of this paper is as follows:1.The PF state-space model of WSN is established,and the Bayesian method of target tracking includes the state-space model needed in order to describe the target dynamics and the state-related measurements.PF is suitable for handling general state-space models and do not depend on the assumption of linear or Gaussian posterior density.The PF algorithm is first described for state estimation tasks.For poor initial conditions,the Markov chain Monte Carlo method is utilized to increase PF's estimation accuracy.2.The PF algorithm is excellent in handling non-linear,non-Gaussian wireless positioning.However,due to the degradation of PF algorithm,it will affect its stable performance.The essential reason is that the reference distribution function cannot be obtained in practical applications.Researching the WSN positioning technology based on PF to avoid the particle degradation and depletion phenomenon in the WSN positioning application is great significance.3.Through the study of PF state space model of WFN,multiple PF algorithms are employed to simulation analysis.The particle quality prediction function and the centroid drift strategy are utilized to repeat PF and generate the final state estimation of the system.The degree of particle degradation and the location estimation value is analyzed.The proposed algorithm and the algorithm are compared in terms of confidence intervals,positioning errors,robustness,etc.It is advantages and improvements.Realizing a robust,high-precision positioning technology for complex environments is still a knotty point in WSN technology.PF transforms the positioning problem into a state estimation problem,and uses Bayesian estimation and other methods to provide new research ideas and methods for wireless sensor network positioning technology.In order to improve the robustness of the particle filter WSN positioning application,the positioning accuracy of the target node in the WSN is further effectively improved,and the positioning error is reduced.The work of this paper enriches the theory of WSN positioning algorithm,and has carried out some useful explorations in its related application fields,which have certain practical value and theoretical significance.