Gossip-Based Distributed Tracking in Networks of Heterogeneous Agents

We consider the distributed tracking problem in networks of heterogeneous agents with limited sensing and communication ranges. A gossip-based distributed Kalman filter (GDKF) is proposed, where an average consensus on predictions of different agents is achieved by randomized, asynchronous gossip al...

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Bibliographic Details
Published inIEEE communications letters Vol. 21; no. 4; pp. 801 - 804
Main Authors Ma, Kangjian, Wu, Shaochuan, Wei, Yuming, Zhang, Wenbin
Format Journal Article
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Agent networks
distributed estimation
Estimation
gossip
Handover
Kalman filter
Kalman filters
Noise measurement
Sensors
Target tracking
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Summary:We consider the distributed tracking problem in networks of heterogeneous agents with limited sensing and communication ranges. A gossip-based distributed Kalman filter (GDKF) is proposed, where an average consensus on predictions of different agents is achieved by randomized, asynchronous gossip algorithms in a totally distributed way. The error dynamics of GDKF is proved to be a globally asymptotically stable system and the error reduction rate is provided. To demonstrate the improved performance of GDKF, we compare it with an alternative distributed estimation strategy termed Kalman-Consensus Filter (KCF) by implementing them to track a maneuvering target collectively with heterogeneous agents.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2016.2637889