Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar
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.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2022
- DOI:
- 10.48550/arXiv.2212.00615
- arXiv:
- arXiv:2212.00615
- Bibcode:
- 2022arXiv221200615T
- Keywords:
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- Electrical Engineering and Systems Science - Signal Processing;
- Computer Science - Machine Learning
- E-Print:
- 5 pages, 3 figures