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

arXiv: https://arxiv.org/abs/2212.00615 · HTML: https://mlsanity.com/paper/2212.00615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Satisficing in Time-Sensitive Bandit Learning](https://mlsanity.com/paper/1803.02855/) (2018)
- [Deciding What to Learn: A Rate-Distortion Approach](https://mlsanity.com/paper/2101.06197/) (2021)
- [Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments](https://mlsanity.com/paper/2006.13173/) (2020)
- [Off-the-shelf sensor vs. experimental radar -- How much resolution is necessary in automotive radar classification?](https://mlsanity.com/paper/2006.05485/) (2020)
- [Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning](https://mlsanity.com/paper/2001.02629/) (2020)
- [Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection](https://mlsanity.com/paper/2103.05541/) (2021)
- [Waveform Design for Mutual Interference Mitigation in Automotive Radar](https://mlsanity.com/paper/2208.04398/) (2022)

## Abstract (arXiv)

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.