Distributed Poisson multi-Bernoulli filtering via generalised covariance intersection

TL;DR

This paper introduces a novel distributed Poisson multi-Bernoulli filter using the generalised covariance intersection rule, providing a tractable approximation that results in a closed-form Poisson multi-Bernoulli mixture, improving multi-object filtering performance.

Contribution

It develops a principled approximation for GCI fusion of PMB densities, enabling closed-form PMBM results and preserving the filter structure across steps.

Findings

The proposed method outperforms existing distributed multi-object filters.

The approximation maintains accuracy while simplifying the fusion process.

Experimental results validate the effectiveness of the approach.

Abstract

This paper presents the distributed Poisson multi-Bernoulli (PMB) filter based on the generalised covariance intersection (GCI) fusion rule for distributed multi-object filtering. Since the exact GCI fusion of two PMB densities is intractable, we derive a principled approximation. Specifically, we approximate the power of a PMB density as an unnormalised PMB density, which corresponds to an upper bound of the PMB density. Then, the GCI fusion rule corresponds to the normalised product of two unnormalised PMB densities. We show that the result is a Poisson multi-Bernoulli mixture (PMBM), which can be expressed in closed form. Future prediction and update steps in each filter preserve the PMBM form, which can be projected back to a PMB density before the next fusion step. Experimental results show the benefits of this approach compared to other distributed multi-object filters.