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arXiv 2005.04708eess.SPcs.LG
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基于强化学习的大规模多输入多输出雷达多目标检测方法

A Reinforcement Learning based approach for Multi-target Detection in Massive MIMO radar

Institute of Digital Communication Systems, Ruhr University Bochum（鲁尔大学波鸿分校数字通信系统研究所）；Université Paris-Saclay（巴黎萨克雷大学）；CentraleSupélec（中央理工-高等电力学院）；DR2I-IPSA（DR2I-IPSA机构）；Università di Pisa（比萨大学）

Aya Mostafa Ahmed, Alaa Alameer Ahmad, Stefano Fortunati, Aydin Sezgin, Maria S. Greco, Fulvio Gini

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摘要：

研究了大规模多输入多输出（MMIMO）认知雷达（CR）的多目标检测问题。CR的概念基于感知-行动循环，感知并智能地适应动态环境，以最佳地满足特定任务。然而，这通常需要环境模型的先验知识，而这在大多数情况下是不可用的。提出了一种基于强化学习（RL）的认知多目标检测算法。雷达充当连续感测未知环境的代理（即，目标和干扰）并因此优化发射波形以便通过将能量聚焦在特定的距离-角度单元（即，波束成形）。此外，我们提出了一种解决波束形成优化问题的方法，其复杂度低于现有的方法。数值模拟进行评估所提出的RL为基础的算法在静态和动态环境中的性能。基于RL的波束形成相比，传统的全向方法具有相等的功率分配和自适应波束形成没有RL。正如所提出的数值结果所强调的那样，我们的基于RL的波束形成器在目标检测性能方面优于这两种方法。在环境恶劣的条件下，如低信噪比，重尾干扰和快速变化的情况下，性能改善甚至特别显着。

英文摘要：

This paper considers the problem of multi-target detection for massive multiple input multiple output (MMIMO) cognitive radar (CR). The concept of CR is based on the perception-action cycle that senses and intelligently adapts to the dynamic environment in order to optimally satisfy a specific mission. However, this usually requires a priori knowledge of the environmental model, which is not available in most cases. We propose a reinforcement learning (RL) based algorithm for cognitive multi-target detection in the presence of unknown disturbance statistics. The radar acts as an agent that continuously senses the unknown environment (i.e., targets and disturbance) and consequently optimizes transmitted waveforms in order to maximize the probability of detection ($P_\mathsf{D}$) by focusing the energy in specific range-angle cells (i.e., beamforming). Furthermore, we propose a solution to the beamforming optimization problem with less complexity than the existing methods. Numerical simulations are performed to assess the performance of the proposed RL-based algorithm in both stationary and dynamic environments. The RL based beamforming is compared to the conventional omnidirectional approach with equal power allocation and to adaptive beamforming with no RL. As highlighted by the proposed numerical results, our RL-based beamformer outperforms both approaches in terms of target detection performance. The performance improvement is even particularly remarkable under environmentally harsh conditions such as low SNR, heavy-tailed disturbance and rapidly changing scenarios.

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