Reinforcement Learning Trained Observer Control for Bearings-Only Tracking

• URL: http://arxiv.org/abs/2605.02120v1
• Date: Mon, 04 May 2026 00:55:14 GMT
• Title: Reinforcement Learning Trained Observer Control for Bearings-Only Tracking
• Abstract summary: This paper develops a reinforcement learning based observer control policy for autonomous bearings-only tracking of a moving target.<n>The reward function is designed to address two conflicting objectives: minimising absolute target position estimation error and maintaining CKF estimation consistency.<n>The results show that the DQN policy at $= 0.7$ achieves the best trade-off between accuracy and robustness.
• Score: 0.9167082845109437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper develops a deep reinforcement learning based observer control policy for autonomous bearings-only tracking of a moving target. The observer manoeuvre problem is formulated as a belief Markov decision process, where the belief state is represented by the posterior of a cubature Kalman filter (CKF). The reward function is designed to address two conflicting objectives: minimising the absolute target position estimation error (Euclidean distance) and maintaining CKF estimation consistency (Mahalanobis distance). The reward is formulated as a geometric interpolation between the two objectives on the Pareto front, parametrised by a weighting factor $β\in [0,1]$. The policy is implemented as a deep Q-network (DQN) trained over 50,000 episodes. Performance is evaluated over 5,000 Monte Carlo episodes and compared against two baselines: the perpendicular-to-bearing heuristic and the D-optimal Fisher information maximisation criterion. The results show that the DQN policy at $β= 0.7$ achieves the best trade-off between accuracy and robustness: it matches the information-theoretic baseline on mean tracking accuracy while reducing the worst-case error by nearly a factor of ten, owing to the implicit filter-consistency regularisation provided by the Mahalanobis term in the reward.

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