# When is Cognitive Radar Beneficial?

arXiv: https://arxiv.org/abs/2212.00597 · HTML: https://mlsanity.com/paper/2212.00597/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [Measuring the Reliability of Reinforcement Learning Algorithms](https://mlsanity.com/paper/1912.05663/) (2019)
- [Stochastically Dominant Distributional Reinforcement Learning](https://mlsanity.com/paper/1905.07318/) (2019)
- [Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection](https://mlsanity.com/paper/2103.05541/) (2021)
- [Online Meta-Learning for Scene-Diverse Waveform-Agile Radar Target Tracking](https://mlsanity.com/paper/2110.11450/) (2021)
- [Universal Learning Waveform Selection Strategies for Adaptive Target Tracking](https://mlsanity.com/paper/2202.05294/) (2022)

## Abstract (arXiv)

When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, in which the radar wishes to transmit in the widest unoccupied bandwidth during each pulse repetition interval. Online learning is compared to a fixed rule-based sense-and-avoid strategy. We show that given a simple Markov channel model, the problem can be examined analytically for simple cases via stochastic dominance. Additionally, we show that for more realistic channel assumptions, learning-based approaches demonstrate greater ability to generalize. However, for short time-horizon problems that are well-specified, we find that machine learning approaches may perform poorly due to the inherent limitation of convergence time. We draw conclusions as to when learning-based approaches are expected to be beneficial and provide guidelines for future study.