Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0

Abstract

The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology—dubbed “LC 2.0”—with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.

The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology—dubbed “LC 2.0”—with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.