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Proceedings Paper

Weighted Kullback-Leibler average-based distributed filtering algorithm
Author(s): Kelin Lu; Kuo-Chu Chang; Rui Zhou
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Paper Abstract

This paper considers a distributed filtering problem over a multi-sensor network in which the correlation of local estimation errors is unknown. Recently, this problem was studied by G. Battistelli [1] by developing a data fusion rule to calculate the weighted Kullback-Leibler average of local estimates with consensus algorithms for distributed averaging, where the weighted Kullback-Leibler average is defined as an averaged probability density function to minimize the sum of weighted Kullback-Leibler divergences from the original probability density functions. In this paper, we extends those earlier results by relaxing the prior assumption that all sensors share the same degree of confidence. Furthermore, a novel consensus-based distributed weighting coefficients selection scheme is developed to improve the fusion accuracy, where the weight associated with each sensor is adjusted based on the local estimation error covariance and the ones received from neighboring sensors, so that larger weight values will be assigned to a sensor with higher degree of confidence. Finally, a Monte-Carlo simulation with a 2D tracking system validates the effectiveness of the proposed distributed filtering algorithm.

Paper Details

Date Published: 21 May 2015
PDF: 13 pages
Proc. SPIE 9474, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, 94740U (21 May 2015); doi: 10.1117/12.2177493
Show Author Affiliations
Kelin Lu, Beihang Univ. (China)
Kuo-Chu Chang, George Mason Univ. (United States)
Rui Zhou, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 9474:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV
Ivan Kadar, Editor(s)

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