Detail publikace

Target Tracking Using Distributed Particle-PDA Filter with Sparsity-promoting Likelihood Consensus

Originální název

Target Tracking Using Distributed Particle-PDA Filter with Sparsity-promoting Likelihood Consensus

Anglický název

Target Tracking Using Distributed Particle-PDA Filter with Sparsity-promoting Likelihood Consensus

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

BibTex


@inproceedings{BUT147019,
  author="Rene {Repp} and Pavel {Rajmic} and Florian {Meyer} and Franz {Hlawatsch}",
  title="Target Tracking Using Distributed Particle-PDA Filter with Sparsity-promoting Likelihood Consensus",
  annote="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.",
  address="IEEE",
  booktitle="Proceedings of the 2018 IEEE Statistical Signal Processing Workshop (SSP)",
  chapter="147019",
  doi="10.1109/SSP.2018.8450815",
  howpublished="electronic, physical medium",
  institution="IEEE",
  year="2018",
  month="june",
  pages="653--657",
  publisher="IEEE",
  type="conference paper"
}