Reinforcement learning-based automated target motion analysis in underwater environments
Bibliographic record
Abstract
This study presents an automated target motion analysis (TMA) framework that leverages deep reinforcement learning (DRL) to enhance the accuracy and reliability of target state estimation from SONAR-derived bearing-only measurements in underwater environments. Traditional TMA methods-such as the manual 10-point divider and batch estimation-rely heavily on operator expertise and are susceptible to inaccuracies due to environmental noise and human error. To address these limitations, we employ a Proximal Policy Optimization (PPO)-based agent to automatically and robustly estimate the target speed. A customized TMA simulator was developed to generate diverse underwater scenarios, incorporating variations in target motion and noise levels to ensure the model’s generalization capability. The PPO agent learns to infer target speed directly from sequential bearing data, achieving a strong balance between exploration and exploitation. Experimental results demonstrate that the trained agent provides highly accurate and robust speed estimates, even under realistic noise conditions. This work contributes to the advancement of autonomous maritime surveillance and defense systems by significantly reducing human dependency and improving operational reliability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".