Abstract
For target tracking in underwater wireless sensor networks (WSNs), the contributions of the measured values of each sensor node are different for data fusion, so a better weighted nodes fusion and participation planning mechanism can obtain better tracking performance. A distributed particle filter based target tracking algorithm with Grubbs criterion and mutual information entropy weighted fusion (GMIEW) is proposed in this paper. The Grubbs criterion is adopted to analyze and verify the information obtained by sensor nodes before the information fusion, and accordingly some interference information or error information can be excluded from the data set. In the process of calculating importance weight in particle filter, dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor nodes and the target state is used to reflect the amount of target information provided by sensor nodes, thus a dynamic weighting factor corresponding to each node can be obtained. The simulation results show that the proposed algorithm effectively improves the accuracy of prediction of target tracking system.
Highlights
With the rapid development and maturity of the technologies of chip design and embedded systems, sensors are gradually developed towards miniaturization and integration with perceptual computing and networked communication capabilities
In this paper, we comprehensively discuss the above problems, and propose a distributed particle filter algorithm based on Grubbs criterion and mutual information entropy weighted fusion
THE PROPOSED ALGORITHM In this paper, we proposed an algorithm based on distributed particle filter with Grubbs criterion and mutual information entropy
Summary
With the rapid development and maturity of the technologies of chip design and embedded systems, sensors are gradually developed towards miniaturization and integration with perceptual computing and networked communication capabilities. The task of the positioning module is to estimate the location of the target by using the perceptual information obtained by the selected most appropriate sensor nodes. Due to the limitations of centralized estimation method, such as large amount of calculation, constraints of network structure and poor robustness, this paper adopts distributed computing mode and uses the exchange and coordination of local measurement information between sensor nodes to complete the state estimation of the target. The mutual information entropy between the measured data obtained by the sensor nodes and the target state are used as the weighting factor of the importance weight of the particle filter algorithm. D. TARGET MEASUREMENT MODEL When the target enters the monitoring area, the sensor nodes distributed in the network will detect the signal from the target. The detailed process of the algorithm is given as follows
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