Kullback–Leibler Divergence Based Distributed Cubature Kalman Filter and Its Application in Cooperative Space Object Tracking
1
Xi’an Institute of High-Tech, Xi’an 710025, Shaanxi, China
2
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(2), 116; https://doi.org/10.3390/e20020116
Received: 17 December 2017
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Revised: 23 January 2018
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Accepted: 8 February 2018
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Published: 10 February 2018
(This article belongs to the Special Issue Radar and Information Theory)
In this paper, a distributed Bayesian filter design was studied for nonlinear dynamics and measurement mapping based on Kullback–Leibler divergence. In a distributed structure, the nonlinear filter becomes a challenging problem, since each sensor cannot access the global measurement likelihood function over the whole network, and some sensors have weak observability of the state. To solve the problem in a sensor network, the distributed Bayesian filter problem was converted into an optimization problem by maximizing a posterior method. The global cost function over the whole network was decomposed into the sum of the local cost function, where the local cost function can be solved by each sensor. With the help of the Kullback–Leibler divergence, the global estimate was approximated in each sensor by communicating with its neighbors. Based on the proposed distributed Bayesian filter structure, a distributed cubature Kalman filter (DCKF) was proposed. Finally, a cooperative space object tracking problem was studied for illustration. The simulation results demonstrated that the proposed algorithm can solve the issues of varying communication topology and weak observability of some sensors.
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Keywords:
cooperative space object tracking; distributed sensor network; distributed estimation; cubature Kalman filter; Kullback–Leibler divergence; consensus
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MDPI and ACS Style
Hu, C.; Lin, H.; Li, Z.; He, B.; Liu, G. Kullback–Leibler Divergence Based Distributed Cubature Kalman Filter and Its Application in Cooperative Space Object Tracking. Entropy 2018, 20, 116. https://doi.org/10.3390/e20020116
AMA Style
Hu C, Lin H, Li Z, He B, Liu G. Kullback–Leibler Divergence Based Distributed Cubature Kalman Filter and Its Application in Cooperative Space Object Tracking. Entropy. 2018; 20(2):116. https://doi.org/10.3390/e20020116
Chicago/Turabian StyleHu, Chen, Haoshen Lin, Zhenhua Li, Bing He, and Gang Liu. 2018. "Kullback–Leibler Divergence Based Distributed Cubature Kalman Filter and Its Application in Cooperative Space Object Tracking" Entropy 20, no. 2: 116. https://doi.org/10.3390/e20020116
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