Reinforcement Learning Trained Observer Control for Bearings-Only Tracking
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 implem...
| Authors | Branko Ristic, Sanjeev Arulampalam |
|---|---|
| Primary category | cs.AI |
| Published | 4 May 2026 |
| Last updated at source | 4 May 2026 |
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