Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance

• URL: http://arxiv.org/abs/2607.22667v1
• Date: Fri, 03 Jul 2026 14:26:26 GMT
• Title: Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance
• Abstract summary: This paper presents an information-gain-guided reinforcement-learning framework for single-vessel tracking in maritime sensor networks.<n>A learned policy selects one tracking-relevant sensor at each decision epoch.<n>Results show that the learned policy achieves tracking performance close to always-on sensing.
• Score: 0.3914676152740142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an information-gain-guided reinforcement-learning sensor-selection framework for single-vessel tracking in heterogeneous maritime sensor networks. The proposed approach is motivated by information-theoretic sensor management: instead of activating all sensors or repeatedly performing computationally expensive online expected-information-gain evaluation, a learned policy selects one tracking-relevant sensor at each decision epoch. A Bayesian sequential Monte Carlo tracker estimates the vessel state from noisy measurements and provides a belief representation for scheduling under nonlinear and non-Gaussian conditions. A Proximal Policy Optimization agent selects one of five sensors deployed in a georeferenced simulation of the CMMI Smart Marina testbed at Ayia Napa Marina, Cyprus. The agent observes belief-state, detection-history, coverage, sensor-geometry, and realized-information-gain features. The reward is defined as a realized-information-gain term gated by an observability mask. Final-test simulations compare the proposed framework with random single-sensor selection, always-on sensing using all sensors simultaneously, and the expected-information-gain sensor-selection baseline proposed in our previous work. Results show that the learned policy achieves tracking performance close to always-on sensing while activating only one sensor per decision time step and avoiding the computationally expensive online entropy search required by expected-information-gain selection.

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