Optimizing sonobuoy placement using multiobjective machine learning

We present a new approach to finding optimal patterns for the placement of fields of sonobuoys in a complex undersea environment. The problem is modelled as a biobjective one, where the aim is to both minimize uncertainty over target localization and minimize sensor placement time. We develop a two-...

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Bibliographic Details
Published in2022 Sensor Signal Processing for Defence Conference (SSPD) pp. 1 - 5
Main Authors Taylor, Christopher M, Maskell, Simon, Ralph, Jason F
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2022
Subjects
Biological system modeling
Location awareness
Machine learning algorithms
Reinforcement learning
Sensor placement
Signal processing algorithms
Uncertainty
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DOI10.1109/SSPD54131.2022.9896216

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Summary:We present a new approach to finding optimal patterns for the placement of fields of sonobuoys in a complex undersea environment. The problem is modelled as a biobjective one, where the aim is to both minimize uncertainty over target localization and minimize sensor placement time. We develop a two-phase algorithm, where an offline multiobjective evolutionary phase finds initial Pareto-nondominated solutions to a static problem, and then an online multiobjective reinforcement learning phase finds improved solutions using updated information. Initial results show that our approach generates significant improvements over standard grid patterns.
DOI:10.1109/SSPD54131.2022.9896216