A cooperative iterative optimization method for wireless sensor network positioning calculation

Abstract

The invention discloses a wireless sensor network positioning and computing method for collaborative iterative optimization. Unknown node coordinates are determined with an algebraic computing method by utilizing a mutual restricted relationship of the distances among nodes. Wireless sensor network nodes on a two-dimensional or three-dimensional plane comprise beacon nodes at known positions and unknown nodes at unknown positions. The method comprises the following steps: firstly adopting the beacon nodes at known positions to position the coordinates of the unknown nodes; taking the positioned unknown nodes as pseudo beacon nodes to position the remaining known nodes; adopting adjacent beacon nodes with the maximum quantity and the pseudo beacon nodes to improve the initial positioning result in the second step; optimizing the positioning result in the third step in a collaborative iterative manner so as to obtain the final coordinates of all known nodes. According to the positioning and computing method, the complicated positioning and computing process is resolved, and the computing process is distributed on each node so as to realize distributed positioning and computing. Simulation results show that the positioning result obtained by the positioning and computing method is restricted, stable and reliable, and high in positional accuracy.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor network positioning, and relates to a wireless sensor network positioning calculation method based on cooperative iterative optimization. Background technique [0002] The wireless sensor network is a wireless communication network composed of a self-organizing way by densely distributing a large number of tiny intelligent nodes with sensor units, data processing units and communication modules in the sensing area. The wireless sensor network can monitor, perceive and collect information of various environments or monitoring objects in the network distribution area in real time, and process the information, so as to provide detailed and accurate information for remote users. Using wireless sensor networks for information collection and processing, these data must be combined with location information to be meaningful, and sometimes even require sensor nodes to send back pure location infor...

AI Technical Summary

Problems solved by technology

Lightweight positioning algorithms such as DV-Hop algorithm, centroid algorithm, APIT algorithm, etc., the calculation process is simple, and it is easy to realize distributed positioning calculation, but the positioning accuracy is not high enough

The maximum likelihood (ML) estimation method based on prior statistical information can reach the CRLB lower bound of the positioning result, and the positioning accuracy is high, but the ML estimation method is a nonlinear problem, and its accurate solution process can only be solved by numerical methods. It is also more complex and prone to singular solutions

In order to improve the positioning accuracy, there are also studies that attribute the positioning model to a mathematical optimization problem. For example, the positioning problem is relaxed into a semi-positive definite programming (SDP) optimization problem. The SDP algorithm can achieve global optimization, and the positioning accuracy is also high, but the computational complexity is high. , it is difficult to realize distributed positioning calculation, and it is not sensitive to changes in the local ranging information of the network, and requires the collection of all network information, so the communication cost is high and the scalability is weak

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