Šauša, E., Rajmic, P., & Hlawatsch, F. (2024). Likelihood Consensus 2.0: Reducing Interagent Communication in Distributed Bayesian Target Tracking. In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 13006–13010). IEEE. https://doi.org/10.1109/ICASSP48485.2024.10447108
We propose a communication-efficient scheme for distributed Bayesian target tracking (distributed particle filtering) in possibly nonlinear and non-Gaussian state-space models. The scheme is a sparsity-promoting evolution of the likelihood consensus (LC) that uses the orthogonal matching pursuit (OMP), a B-spline dictionary, a distributed adaptive determination of the relevant state-space region, and an efficient binary representation of the LC expansion coefficients. Our simulation results show that a reduction of interagent communication by a factor of about 190 can be obtained without compromising the tracking performance.
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Project title:
Selbstlokalisierung und Inferenz dynamischer Umgebungen: P 32055-N31 (FWF - Österr. Wissenschaftsfonds)
CC BY 4.0
