A multi-sensor multi-Bernoulli filter

TL;DR

This paper introduces an efficient multi-sensor multi-Bernoulli filter for multi-target tracking, improving accuracy and reducing computational load compared to existing methods, especially in low detection scenarios.

Contribution

It develops a novel approximate implementation of the MS-MeMBer filter using greedy measurement partitioning, applicable to Gaussian mixture and particle filter frameworks.

Findings

Enhanced tracking accuracy over existing filters.

Reduced computational complexity in multi-sensor scenarios.

Effective performance in low probability of detection conditions.

Abstract

In this paper we derive a multi-sensor multi-Bernoulli (MS-MeMBer) filter for multi-target tracking. Measurements from multiple sensors are employed by the proposed filter to update a set of tracks modeled as a multi-Bernoulli random finite set. An exact implementation of the MS-MeMBer update procedure is computationally intractable. We propose an efficient approximate implementation by using a greedy measurement partitioning mechanism. The proposed filter allows for Gaussian mixture or particle filter implementations. Numerical simulations conducted for both linear-Gaussian and non-linear models highlight the improved accuracy of the MS-MeMBer filter and its reduced computational load with respect to the multi-sensor cardinalized probability hypothesis density filter and the iterated-corrector cardinality-balanced multi-Bernoulli filter especially for low probabilities of detection.