Reinforcement learning enhances autonomous target tracking accuracy and robustness

Researchers have developed a deep reinforcement learning approach for autonomous bearings-only tracking of moving targets. The system formulates the observer maneuver problem as a belief Markov decision process, using a Cubature Kalman Filter to represent the belief state. A reward function balances minimizing estimation error with maintaining filter consistency, and the policy was trained using a deep Q-network over 50,000 episodes. AI

影响 Introduces a novel RL-based control policy for improved target tracking accuracy and robustness in bearings-only scenarios.

排序理由 This is a research paper detailing a novel application of reinforcement learning to a specific tracking problem. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• arXiv
• deep Q-network
• Markov decision process
• Cubature Kalman Filter

• 论文
• 其他

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →