Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection

TL;DR

This paper introduces a novel distributed multi-object tracking algorithm that fuses multi-Bernoulli filters using generalized Covariance Intersection, employing approximations and Monte Carlo methods to improve accuracy and scalability in sensor networks.

Contribution

The paper presents a new fusion method for multi-Bernoulli filters based on G-CI, with novel approximations to handle intractable distributions and enable sequential fusion in distributed sensor networks.

Findings

The proposed algorithm effectively fuses multi-object tracking data from multiple sensors.

Numerical results demonstrate improved tracking accuracy and robustness.

The method is scalable and suitable for real-time applications in sensor networks.

Abstract

In this paper, we propose a distributed multi-object tracking algorithm through the use of multi-Bernoulli (MB) filter based on generalized Covariance Intersection (G-CI). Our analyses show that the G-CI fusion with two MB posterior distributions does not admit an accurate closed-form expression. To solve this challenging problem, we firstly approximate the fused posterior as the unlabeled version of $\delta$-generalized labeled multi-Bernoulli ($\delta$-GLMB) distribution, referred to as generalized multi-Bernoulli (GMB) distribution. Then, to allow the subsequent fusion with another multi-Bernoulli posterior distribution, e.g., fusion with a third sensor node in the sensor network, or fusion in the feedback working mode, we further approximate the fused GMB posterior distribution as an MB distribution which matches its first-order statistical moment. The proposed fusion algorithm is implemented using sequential Monte Carlo technique and its performance is highlighted by numerical results.