The joint detection and tracking of a moving target embedded in an unknown disturbance represents a key feature that motivates the development of the cognitive radar paradigm. Building upon recent advancements in robust target detection with multiple-input multiple-output (MIMO) radars, this work explores the application of a Partially Observable Markov Decision Process (POMDP) framework to enhance the tracking and detection tasks in a statistically unknown environment. In the POMDP setup, the radar system is considered as an intelligent agent that continuously senses the surrounding environment, optimizing its actions to maximize the probability of detection $(P_D)$ and improve the target position and velocity estimation, all this while keeping a constant probability of false alarm $(P_{FA})$. The proposed approach employs an online algorithm that does not require any apriori knowledge of the noise statistics, and it relies on a much more general observation model than the traditional range-azimuth-elevation model employed by conventional tracking algorithms. Simulation results clearly show substantial performance improvement of the POMDP-based algorithm compared to the State-Action-Reward-State-Action (SARSA)-based one that has been recently investigated in the context of massive MIMO (MMIMO) radar systems.

翻译：在未知干扰中对运动目标进行联合检测与跟踪，是推动认知雷达范式发展的关键特性。基于多输入多输出雷达在鲁棒目标检测方面的最新进展，本研究探索了应用部分可观测马尔可夫决策过程框架，以在统计特性未知的环境中提升跟踪与检测任务性能。在POMDP框架中，雷达系统被视为一个智能体，持续感知周围环境，并优化其行动以最大化检测概率$(P_D)$，同时改善目标位置与速度估计，且始终保持恒定的虚警概率$(P_{FA})$。所提出的方法采用一种在线算法，无需任何噪声统计的先验知识，并且依赖比传统跟踪算法所使用的距离-方位-仰角模型更为通用的观测模型。仿真结果明确显示，与近期在大规模MIMO雷达系统中研究的基于状态-动作-奖励-状态-动作的算法相比，基于POMDP的算法性能有显著提升。