Particle weight approximation with clustering for gossip-based distributed particle filters
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)(2015)
摘要
Distributed particle filters are appealing for cooperative tracking in distributed systems. However, a non-negligible amount of communication overhead can be required to synchronize the particle weights between agents. This paper proposes a particle weight approximation method, based on clustering and a smoothness assumption on the particle cloud distribution, to reduce the communication overhead. The proposed algorithm is evaluated on both simulated data and data from an at-sea trial involving bearings-only tracking. The results demonstrate that the proposed approach achieves state-of-the-art accuracy, especially in cases with a limited communication budget.
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关键词
gossip-based distributed particle filters,cooperative tracking,distributed systems,communication overhead,particle weight approximation method,smoothness assumption,particle cloud distribution,communication overhead,bearings-only tracking,limited communication budget
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