论文标题
基于在线学习的波形选择，以改善汽车雷达的车辆识别
Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar
论文作者
论文摘要
本文介绍了与在线加强学习的重要考虑因素和挑战相关的波形选择，用于频率调制连续波（FMCW）汽车雷达系统中的目标识别。我们提出了一种基于汤普森采样的焦糖学习方法的新型学习方法，该方法迅速确定了预期的波形，可以产生令人满意的分类性能。我们通过测量级模拟证明，即使在雷达必须从大型候选波形目录中选择的情况下，也可以快速学习有效的波形选择策略。雷达学会通过优化预期的分类度量，以适当的分辨率和缓慢的分辨率进行适当分辨率的带宽和缓慢缓解的较慢的单模型代码。
This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection for target identification in frequency modulated continuous wave (FMCW) automotive radar systems. We present a novel learning approach based on satisficing Thompson sampling, which quickly identifies a waveform expected to yield satisfactory classification performance. We demonstrate through measurement-level simulations that effective waveform selection strategies can be quickly learned, even in cases where the radar must select from a large catalog of candidate waveforms. The radar learns to adaptively select a bandwidth for appropriate resolution and a slow-time unimodular code for interference mitigation in the scene of interest by optimizing an expected classification metric.
