Wireless sensor network target tracking method based on informational consistency

Abstract

The invention discloses a wireless sensor network target tracking method based on informational consistency. The method includes: (1) initiating a prior information vector and a prior information matrix of each node; (2) calculating an observation vector of each node; (3) calculating a local observation vector and a local observation matrix of each node; (4) enabling each node to broadcast information to adjacent nodes; (5) calculating a fused local observation vector and a fused local observation matrix of each node; (6) calculating a consistency fusion local information vector and consistency fusion local information matrix of each node; (7) acquiring an information vector consistency estimation and an information matrix consistency estimation of each node; (8) acquiring a predictive value and an estimation value of a target state of each node; and (9) predicting and updating the prior information vector and the prior information matrix of each node. Information filtering and consistency strategies are combined, and accordingly accuracy and consistency of state estimation of each node in a sensor network are improved.

Description

technical field [0001] The invention belongs to the field of sensor network target tracking, in particular to a wireless sensor network target tracking method based on information consistency. Background technique [0002] In a wireless sensor network, multiple sensor nodes observe the state of the observed target (such as the target's orientation, movement speed, etc.), and use various state estimation algorithms to obtain the estimated value of the target state from the noise-contaminated observations. . In order to improve the performance of each node state estimation, the traditional method is to collect the observation information or local estimation information of all nodes through the fusion center for information fusion processing. Centralized Kalman filter algorithm (CKF) is a classic method based on the fusion center. However, due to the limitation of network structure and communication capacity, these fusion center-based algorithms need to spend a lot of overhea...

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Problems solved by technology

However, KCF uses consistent weighting coefficients determined only by the network topology to fuse the estimated information of neighbor nodes, while ignoring the uncertainty of neighbor node estimates.

In fact, due to the differences in the thermal noise of the nodes themselves and the perceived environment, the uncertainty of the local estimation of different nodes is usually different, especially in the initial stage of the algorithm, so the estimation error covariance matrix M of KCF i (k) did not improve

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