Bernoulli Filtering for Multi-Sensor Tracking with Thresholded Measurements

Gustav Zetterqvist; Fredrik Gustafsson; Gustaf Hendeby

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arXiv · 2606.09573

Bernoulli Filtering for Multi-Sensor Tracking with Thresholded Measurements

Gustav Zetterqvist·Fredrik Gustafsson·Gustaf Hendeby

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Abstract

Target tracking is challenging when sensor detection thresholds cause state-dependent missed detections, particularly in multi-sensor scenarios with clutter and uncertain target existence. A recently developed missed detection framework models detection probability as a function of target state, sensor characteristics, and detection threshold, but it is limited to individual measurements and does not address the recursive tracking problem. This work extends the framework using a Bernoulli filter formulation to jointly handle recursive target tracking, clutter, and target existence uncertainty. A Bernoulli particle filter is evaluated in a simulated 2D multi-sensor tracking scenario with nonlinear measurements, clutter, and detection uncertainty. Incorporating accurate detection threshold knowledge reduces the generalized optimal subpattern assignment (GOSPA) metric by 62.4% compared to a conventional Bernoulli filter with fixed detection probability, while better balancing missed detections and false alarms.

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BibTeX RIS

Gustav Zetterqvist, Fredrik Gustafsson, Gustaf Hendeby. 2026-06-08. Bernoulli Filtering for Multi-Sensor Tracking with Thresholded Measurements. https://arxiv.org/abs/2606.09573

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