Low-complexity Wireless Sensor Network Location Algorithm Based on Bayesian Hierarchical Model
In this paper,the algorithm of wireless sensor network location based on compression perception is studied,and there are some problems such as large computation of reconstruction algorithm and large positioning error.In order to reduce the computational complexity and localization error,a low complexity wireless sensor network localization algorithm based on Bayesian Hierarchical Model is proposed.Firstly,the sparse Bayesian hierarchical priori model is introduced into the positioning of wireless sensor networks.Secondly,by using sparse Bayesian theory,the transcendental probability distribution of estimated target is deduced.Finally,combined with Variational Message Passing(VMP)algorithm,the joint posterior probability density function of unknown variables is equivalent by auxiliary function,and the target location vector is estimated.The simulation results show that the proposed method in this paper has better recovery effect and lower computational complexity than traditional reconstruction algorithm.
国家科技期刊平台
NSTL
万方数据
维普
