A multi-sensor multi-Bernoulli filter

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.

21 pages, 8 figures, 2 table

References in corpus (5)

• Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection
• A multi-sensor multi-Bernoulli filter
• Multisensor CPHD filter
• The Marginalized $\delta$-GLMB Filter
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