Variational Bayesian expectation maximization positioning method and system for wireless sensor network

Abstract

The invention discloses a variational Bayesian expectation maximization positioning method and system for a wireless sensor network, and the method comprises the steps: carrying out the positioning ofa wireless sensor network with a cavity, and obtaining positioning information; determining a node on a hole boundary according to the positioning information; obtaining distance information betweenthe nodes in a multi-hop mode; determining bevel information of each node; establishing a maximum likelihood estimation model with an implicit variable according to the distance information and the bevel information; establishing a variational Bayesian expectation maximization positioning model according to the maximum likelihood estimation model; updating the parameters of the variational Bayesian expectation maximization positioning model, adjusting the estimated position of the unknown node, and obtaining the position information of the unknown node after multiple iterations. According to the invention, the positioning precision of the wireless sensor network can be improved.

Description

technical field [0001] The invention relates to the field of wireless sensor network node positioning, in particular to a wireless sensor network variational Bayesian expectation maximization positioning method and system. Background technique [0002] Wireless sensor network (wireless sensor networks, WSNs) node positioning technology is an important supporting technology to realize advanced scientific concepts such as big data analysis and Internet of Things applications. Therefore, it is of great significance to use wireless sensor network information to locate sensor nodes. At present, the research on positioning technology for uniformly distributed wireless sensor networks is relatively mature. The basic positioning methods include DV-HOP, centroid algorithm, APIT and other non-ranging methods, as well as TOA, AOA and other ranging methods. Based on the above methods, , some more accurate and complex methods including collaborative methods, distributed methods, convex ...

AI Technical Summary

Problems solved by technology

[0004] However, compared with the requirements of wireless sensor networks in practical applications, the positioning accuracy of existing methods cannot meet the standard, and the large deviation of positioning results will cause serious obstacles to data processing and decision-making execution, which is not conducive to wireless sensor networks. Application in practical engineering

In addition, existing methods have high requirements on sensor hardware, communication conditions, and node computing capabilities, which increases the difficulty of positioning large-scale wireless sensor networks in complex environments.

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