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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Veröffentlicht in:IEEE communications letters 2017-04, Vol.21 (4), p.801-804
Hauptverfasser: Ma, Kangjian, Wu, Shaochuan, Wei, Yuming, Zhang, Wenbin
Format: Artikel
Sprache:eng
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Zusammenfassung: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