# Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection

arXiv: https://arxiv.org/abs/2103.05541 · HTML: https://mlsanity.com/paper/2103.05541/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [An Information-Theoretic Analysis of Thompson Sampling](https://mlsanity.com/paper/1403.5341/) (2014)
- [Joint Radar and Communication Design: Applications, State-of-the-art, and the Road Ahead](https://mlsanity.com/paper/1906.00789/) (2019)
- [Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments](https://mlsanity.com/paper/2006.13173/) (2020)
- [Radar and Communication Co-existence: an Overview](https://mlsanity.com/paper/1902.08676/) (2019)
- [Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning](https://mlsanity.com/paper/2001.02629/) (2020)
- [Cognitive Radar Antenna Selection via Deep Learning](https://mlsanity.com/paper/1802.09736/) (2018)
- [Interfering Channel Estimation in Radar-Cellular Coexistence: How Much Information Do We Need?](https://mlsanity.com/paper/1807.04000/) (2018)
- [Semi-Blind Post-Equalizer SINR Estimation and Dual CSI Feedback for Radar-Cellular Coexistence](https://mlsanity.com/paper/2006.01327/) (2020)
- [Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling](https://mlsanity.com/paper/2008.10149/) (2020)

## Built on by (most cited first, 5 of 5)

- [Universal Learning Waveform Selection Strategies for Adaptive Target Tracking](https://mlsanity.com/paper/2202.05294/) (2022)
- [Online Bayesian Meta-Learning for Cognitive Tracking Radar](https://mlsanity.com/paper/2207.06917/) (2022)
- [When is Cognitive Radar Beneficial?](https://mlsanity.com/paper/2212.00597/) (2022)
- [Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar](https://mlsanity.com/paper/2212.00615/) (2022)
- [Model-Adaptive and Risk-Constrained Frequency Hopping Against Predictive Jammers](https://mlsanity.com/paper/2609.06514/) (2026)

## Abstract (arXiv)

A sequential decision process in which an adaptive radar system repeatedly interacts with a finite-state target channel is studied. The radar is capable of passively sensing the spectrum at regular intervals, which provides side information for the waveform selection process. The radar transmitter uses the sequence of spectrum observations as well as feedback from a collocated receiver to select waveforms which accurately estimate target parameters. It is shown that the waveform selection problem can be effectively addressed using a linear contextual bandit formulation in a manner that is both computationally feasible and sample efficient. Stochastic and adversarial linear contextual bandit models are introduced, allowing the radar to achieve effective performance in broad classes of physical environments. Simulations in a radar-communication coexistence scenario, as well as in an adversarial radar-jammer scenario, demonstrate that the proposed formulation provides a substantial improvement in target detection performance when Thompson Sampling and EXP3 algorithms are used to drive the waveform selection process. Further, it is shown that the harmful impacts of pulse-agile behavior on coherently processed radar data can be mitigated by adopting a time-varying constraint on the radar's waveform catalog.