Monte Carlo Localization For WSN Based On Crossover And Mutation

Since the wireless sensor network was born in the last century 90's,its application research in various fields has never been interrupted.WSN node localization can provide the position information of the node,which is the fundamental supporting technology of node clustering,routing and energy optimization.At the present stage,the research on node localization is mostly focused on the static network environment,there is very little research on the dynamic network.With the increasing application of WSN in mobile scenarios(such as military reconnaissance,intelligent transportation,etc.),it is urgent to study the localization algorithm of WSN nodes in mobile environment.Monte Carlo Localization(MCL)was originally applied to the robot localization,and then used for WSN mobile node localization,and achieved a better positioning effect.However,MCL is a kind of particle filter,the biggest flaw of particle filter is the particle degeneracy phenomenon.Although,to some extent,the introduction of resampling technique has improved the phenomenon of particle degradation,there are new problems,namely,the loss of particle diversity.Due to the diversity of the sample set becomes worse,it is difficult to approximate the posterior probability density of the node location,which makes it difficult to improve the estimation accuracy of the unknown node position.In view of the above problems,this paper proposes a multi-hop Monte Carlo localization(GMMCL)algorithm based on genetic crossover and mutation.Namely,to move priori particles to a high likelihood region by using the unique ability of optimization of genetic crossover and mutation.Thus,relieves the particle degradation,improves the diversity of samples.Increasing the estimation accuracy of node position.The structure of thesis is organized as follows: Research background,structure,characteristics,key technologies of WSN and the research significance of mobile WSN node localization are introduced in chapter 1.Some common typical algorithms of mobile node localization in WSN are introduced,and focuses on the description of the localization algorithms based on statistical methods(such as MCL,MCB,etc.),and the performance of these algorithms are compared with each other.Finally,some other algorithms which can be applied to the location of mobile nodes are simply introduced in chapter 2.First,a relatively in-depth description is carried out for the Bayesian Estimation and Particle Filter which are the theoretical basis of localization algorithm.Then,the genetic algorithm is introduced,and its unique advantage in searching for the optimal solution is described.Finally,a MMCL algorithm based on crossover and mutation(GMMCL)is proposed,and its feasibility is verified theoretically in chapter 3.The proposed algorithm is simulated and verified,and then compared with the existing algorithms from different aspects in chapter 4.The conclusion summarizes the full text,and puts forward the deficiency of this paper,and the development direction is prospected.In simulation respect,this paper analyzes the change of location error of unknown node position with the number of anchor,the maximum moving speed of node,the sample size and the density of node,meanwhile,compares and analyzes with MCL,MCB and MMCL algorithm.Simulation results show that,compared with other three algorithms,the positioning accuracy of the GMMCL algorithm proposed in this paper is improved to a certain extent.