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This work extends no-regret learning for FMCW radar interference avoidance, improving range resolution. It suggests a method to navigate dense traffic conditions effectively.
Nonlinear frequency hopping combined with no-regret learning has emerged as a promising approach for mitigating interference in automotive FMCW radar systems. However, in dense traffic scenarios, these techniques are challenged by the constraint that each radar has to sample at least one subband per chirp. To enhance range resolution and interference mitigation, we extend No-Regret Hopping by expanding the strategy space along the time dimension, such that every radar can utilize the full available bandwidth-even when the number of radars exceeds the number of partitioned subbands.
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Pan et al. (2025) studied this question.
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