Reinforcement Learning for Heterogeneous Sensor Selection in Maritime Surveillance

2026-07-03

AI Summary

本文通过信息增益引导的强化学习方法选择单船跟踪中的最佳传感器，以减少计算成本同时保持跟踪性能。

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.

Problem

• Reinforcement Learning
• Heterogeneous Sensor Selection
• Maritime Surveillance
• Information Gain
• Single-Vessel Tracking

Innovation

• Reinforcement Learning
• Information Gain
• Sensor Selection
• Maritime Surveillance
• Bayesian Tracking