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arXiv · 2405.05815

Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management

George Jones·Angel Garcia-Fernandez·Christian Blackman
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Abstract

In this paper, we propose an algorithm for non-myopic sensor management for Bernoulli filtering, i.e., when there may be at most one target present in the scene. The algorithm is based on selecting the action that solves a Bellman-type minimisation problem, whose cost function is the mean square generalised optimal sub-pattern assignment (GOSPA) error, over a future time window. We also propose an implementation of the sensor management algorithm based on an upper bound of the mean square GOSPA error and a Gaussian single-target posterior. Finally, we develop a Monte Carlo tree search algorithm to find an approximate optimal action within a given computational budget. The benefits of the proposed approach are demonstrated via simulations.

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George Jones, Angel Garcia-Fernandez, Christian Blackman. 2024-05-09. Non-myopic GOSPA-driven Gaussian Bernoulli Sensor Management. https://doi.org/10.1109/taes.2024.3418750

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