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Target Tracking Using a Distributed Particle-Pda Filter With Sparsity-Promoting Likelihood Consensus | IEEE Conference Publication | IEEE Xplore

Target Tracking Using a Distributed Particle-Pda Filter With Sparsity-Promoting Likelihood Consensus


Abstract:

We propose a distributed particle-based probabilistic data association filter (PDAF) for target tracking in the presence of clutter and missed detections. The proposed PD...Show More

Abstract:

We propose a distributed particle-based probabilistic data association filter (PDAF) for target tracking in the presence of clutter and missed detections. The proposed PDAF employs a new “sparsity-promoting” likelihood consensus that uses the orthogonal matching pursuit for a sparse approximation of the local likelihood functions. Simulation results demonstrate that, compared to the conventional likelihood consensus based on least-squares approximation, large savings in intersensor communication can be obtained without compromising the tracking performance.
Date of Conference: 10-13 June 2018
Date Added to IEEE Xplore: 30 August 2018
ISBN Information:
Conference Location: Freiburg im Breisgau, Germany

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