Gaussian implementation of the multi-Bernoulli mixture filter

TL;DR

This paper introduces a Gaussian implementation of the multi-Bernoulli mixture filter for multi-target tracking, providing closed-form solutions under linear/Gaussian models and demonstrating its effectiveness through simulations.

Contribution

The paper develops a Gaussian implementation of the MBM filter that offers closed-form solutions and uses Murty's algorithm for hypothesis selection, advancing multi-target filtering methods.

Findings

Closed-form Gaussian expressions for single target densities.

Effective hypothesis selection using Murty's algorithm.

Competitive performance demonstrated through numerical simulations.

Abstract

This paper presents the Gaussian implementation of the multi-Bernoulli mixture (MBM) filter. The MBM filter provides the filtering (multi-target) density for the standard dynamic and radar measurement models when the birth model is multi-Bernoulli or multi-Bernoulli mixture. Under linear/Gaussian models, the single target densities of the MBM mixture admit Gaussian closed-form expressions. Murty's algorithm is used to select the global hypotheses with highest weights. The MBM filter is compared with other algorithms in the literature via numerical simulations.