Method for tracking targets of wireless sensor network on basis of information consistency weight optimization

Abstract

The invention discloses a method for tracking targets of a wireless sensor network on the basis of information consistency weight optimization. The method includes (1), initializing prior information vectors and prior information matrixes of various nodes; (2), computing observation vectors of the various nodes; (3), computing local observation vectors and local observation matrixes of the various nodes; (4), broadcasting information of the various nodes to neighbor nodes; (5), computing fused local observation vectors and fused local observation matrixes of the various nodes; (6), optimizing consistency weights; (7), computing consistency-fused local information vectors and consistency-fused local information matrixes of the various nodes; (8), acquiring information vector consistency estimation and information matrix consistency estimation of the various nodes; (9), acquiring target state predicted values and target state estimated values of the various nodes; (10), predicting and updating the prior information vectors and the prior information matrixes of the various nodes. The method has the advantage that the state estimation accuracy and the state estimation consistency of the various nodes in the 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 weight optimization. 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 sp...

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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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