Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar

• URL: http://arxiv.org/abs/2212.00615v1
• Date: Thu, 1 Dec 2022 16:10:30 GMT
• Title: Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar
• Authors: Charles E. Thornton, William W. Howard, and R. Michael Buehrer
• Abstract summary: This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection. We present a novel learning approach based on satisficing Thompson sampling, which quickly identifies a waveform expected to yield satisfactory classification performance.
• Score: 8.113163502779175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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