用深度强化学习优化雷达多目标搜索与跟踪，提升复杂环境下的探测能力。
Multi-Target Radar Search and Track Using Sequence-Capable Deep Reinforcement Learning
- 设计3D仿真环境，采用带多头自注意力的循环神经网络处理序列数据。
- 多头自注意力模型在搜追协同任务中表现最优，显著提升跟踪精度。
- 适合研究雷达智能决策、强化学习在传感器管理中应用的学者参考。
本研究针对雷达系统的传感器任务管理问题，提出一种基于强化学习的多目标搜索与跟踪方法。构建了包含有源相控阵雷达的三维仿真环境，结合多目标跟踪算法提升观测数据质量。对比了三种神经网络架构，包括使用门控循环单元与多头自注意力机制的方法。采用行为克隆和自编码器两种预训练技术：前者近似随机搜索策略，后者用于预训练特征提取器。实验显示，各方法在搜索性能上差异较小，但协同搜索与跟踪任务更具挑战性。多头自注意力架构表现最佳，表明序列感知型网络在动态追踪场景中的潜力。核心贡献在于验证了强化学习在优化传感器管理中的有效性，有望提升雷达系统在复杂环境下对多目标的识别与跟踪能力。
原文摘要 · Abstract (English)
The research addresses sensor task management for radar systems, focusing on efficiently searching and tracking multiple targets using reinforcement learning. The approach develops a 3D simulation environment with an active electronically scanned array radar, using a multi-target tracking algorithm to improve observation data quality. Three neural network architectures were compared including an approach using fated recurrent units with multi-headed self-attention. Two pre-training techniques were applied: behavior cloning to approximate a random search strategy and an auto-encoder to pre-train the feature extractor. Experimental results revealed that search performance was relatively consistent across most methods. The real challenge emerged in simultaneously searching and tracking targets. The multi-headed self-attention architecture demonstrated the most promising results, highlighting the potential of sequence-capable architectures in handling dynamic tracking scenarios. The key contribution lies in demonstrating how reinforcement learning can optimize sensor management, potentially improving radar systems' ability to identify and track multiple targets in complex environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。