Best fit of mixture for multi-sensor poisson multi-Bernoulli mixture filtering

Abstract

We propose a computationally efficient, the first so far, multi-sensor extension of the Poisson multi-Bernoulli mixture (PMBM) filter that accommodates both centralized and distributed sensor networks. In the distributed case, a distributed flooding algorithm is needed for internode communication, which iteratively shares the relevant multi-target posterior among neighbor sensors, thereby making each local sensor serve like a fusion center in the centralized case. The PMBM posterior yielded at each sensor is decomposed into two components corresponding to undetected and detected targets which are modelled by Poisson and multi-Bernoulli mixture (MBM) distributions, respectively. Both communication and fusion are performed with regard to the latter only. A “best-fit-of-mixture” fusion principle is adopted at each sensor to find an MBM that best fits the mixture of MBMs from distinct sensors, which results precisely in the arithmetic average (AA) of these MBMs. The information divergence of the AA from the true density is analyzed. We also provide a Bayesian model averaging interpretation of the MBM-AA fusion. Simulations in scenarios of different target detection probabilities demonstrate the performance of the proposed PMBM filter in terms of localization error, false-alarm/misdetection errors, and communication and computation costs, in comparison with the AA-based multisensor multi-Bernoulli filter.