Histogram-probabilistic multi-hypothesis tracking with a Poisson mixture measurement process

Lukas Herrmann; Edmund Brekke; Egil Eide

doi:10.1117/12.2683817

23 October 2023 Histogram-probabilistic multi-hypothesis tracking with a Poisson mixture measurement process

Lukas Herrmann, Edmund Brekke, Egil Eide

Proceedings Volume 12736, Target and Background Signatures IX; 127360I (2023) https://doi.org/10.1117/12.2683817
Event: SPIE Security + Defence, 2023, Amsterdam, Netherlands

Abstract

The histogram-probabilistic multi-hypothesis tracker (H-PMHT) is an efficient parametric mixture fitting approach to the multi target track-before-detect (TBD) problem. It has been shown that it can give comparable performance to other methods by a fraction of the computational costs. In the original derivation of the H-PMHT, the mixing proportions are both coupled and uncorrelated over time, which may not hold true in practical scenarios involving fluctuating target amplitudes. In this paper, the mixing proportions are modeled according to a Poisson mixture measurement process. In contrast to existing approaches, a more general Markov chain prior based on the generalized inverse Gaussian (GIG) distribution is used as a prior of the Poisson mixing rates. The proposed method provides an alternative solution to the data association uncertainty in clutter, giving accurate and robust signal-to-noise ratio (SNR) estimates by utilizing the GIG Markov chain. The results are validated on simulated data.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lukas Herrmann, Edmund Brekke, and Egil Eide "Histogram-probabilistic multi-hypothesis tracking with a Poisson mixture measurement process", Proc. SPIE 12736, Target and Background Signatures IX, 127360I (23 October 2023); https://doi.org/10.1117/12.2683817

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