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arXiv · 2504.20526

Histogram-Probabilistic Multi-Hypothesis Tracking with Integrated Target Existence

Lukas Herrmann·Ángel F. García-Fernández·Edmund F. Brekke
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Abstract

The histogram-probabilistic multi-hypothesis tracker (H-PMHT) is a parametric approach to solving the multi-target track-before-detect (TBD) problem, using expectation maximisation (EM). A key limitation of this method is the assumption of a known and constant number of targets. In this paper, we propose the integrated existence Poisson histogram probabilistic multi-hypothesis tracker (IE-PHPMHT), for TBD of multiple targets. It extends the H-PMHT framework by adding a probability of existence to each potential target. For the derivation, we utilise a Poisson point process (PPP) measurement model and Bernoulli targets, allowing for a multi-Bernoulli birth process and an unknown, time-varying number of targets. Hence, integrated track management is achieved through the discrimination of track quality assessments based on existence probabilities. The algorithm is evaluated in a simulation study of two scenarios and is compared with several other algorithms, demonstrating its performance.

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Lukas Herrmann, Ángel F. García-Fernández, Edmund F. Brekke. 2026-07-17. Histogram-Probabilistic Multi-Hypothesis Tracking with Integrated Target Existence. https://doi.org/10.1109/taes.2025.3624188

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