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arXiv 2103.05541cs.ITcs.LGeess.SPmath.IT
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约束上下文Bandit学习自适应雷达波形选择

Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection

US Army Research Laboratory(美国陆军研究实验室)

Charles E. Thornton, R. Michael Buehrer, Anthony F. Martone

摘要:

研究了自适应雷达系统与有限状态目标信道反复交互的序贯判决过程。雷达能够以规则的间隔被动地感测频谱,这为波形选择过程提供了辅助信息。雷达发射机使用频谱观测序列以及来自并置接收机的反馈来选择精确估计目标参数的波形。研究表明,使用线性上下文Bandit公式可以以计算可行且样本高效的方式有效解决波形选择问题。随机和对抗性的线性上下文的强盗模型的介绍,使雷达在广泛的物理环境中实现有效的性能。在雷达通信共存的情况下,以及在对抗雷达干扰机的情况下,仿真表明,所提出的配方提供了一个显着的改善目标检测性能时,汤普森采样和EXP3算法被用来驱动波形选择过程。此外,它表明,脉冲捷变行为的相干处理的雷达数据的有害影响,可以减轻通过采用随时间变化的约束雷达的波形目录。

英文摘要:

A sequential decision process in which an adaptive radar system repeatedly interacts with a finite-state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an adversarial radar-jammer scenario, demonstrate that the proposed formulation provides a substantial improvement in target detection performance when Thompson Sampling and EXP3 algorithms are used to drive the waveform selection process. Further, it is shown that the harmful impacts of pulse-agile behavior on coherently processed radar data can be mitigated by adopting a time-varying constraint on the radar's waveform catalog.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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