# Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments

arXiv: https://arxiv.org/abs/2006.13173 · HTML: https://mlsanity.com/paper/2006.13173/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection](https://mlsanity.com/paper/2103.05541/) (2021)
- [Universal Learning Waveform Selection Strategies for Adaptive Target Tracking](https://mlsanity.com/paper/2202.05294/) (2022)
- [Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning](https://mlsanity.com/paper/2203.12048/) (2022)
- [Waveform Selection for Radar Tracking in Target Channels With Memory via Universal Learning](https://mlsanity.com/paper/2108.01181/) (2021)
- [Aircraft Radar Altimeter Interference Mitigation Through a CNN-Layer Only Denoising Autoencoder Architecture](https://mlsanity.com/paper/2410.03423/) (2024)
- [Learning-Based Resource Management in Integrated Sensing and Communication Systems](https://mlsanity.com/paper/2506.20849/) (2025)
- [Online Bayesian Meta-Learning for Cognitive Tracking Radar](https://mlsanity.com/paper/2207.06917/) (2022)
- [Online Learning-based Waveform Selection for Improved Vehicle Recognition in Automotive Radar](https://mlsanity.com/paper/2212.00615/) (2022)
- [An Index Policy Based on Sarsa and Q-learning for Heterogeneous Smart Target Tracking](https://mlsanity.com/paper/2402.12015/) (2024)
- [Adaptive LPD Radar Waveform Design with Generative Deep Learning](https://mlsanity.com/paper/2403.12254/) (2024)
- [Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management](https://mlsanity.com/paper/2506.20853/) (2025)
- [Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning](https://mlsanity.com/paper/2506.20916/) (2025)
- [Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model](https://mlsanity.com/paper/2509.11016/) (2025)
- [Constrained Deep Reinforcement Learning for Cognitive Radar Resource Management](https://mlsanity.com/paper/2606.05526/) (2026)
- [A Contraction Framework for Stochastic Operators with Bootstrapping: Application to TD Learning](https://mlsanity.com/paper/2609.29961/) (2026)

## Abstract (arXiv)

In this paper, dynamic non-cooperative coexistence between a cognitive pulsed radar and a nearby communications system is addressed by applying nonlinear value function approximation via deep reinforcement learning (Deep RL) to develop a policy for optimal radar performance. The radar learns to vary the bandwidth and center frequency of its linear frequency modulated (LFM) waveforms to mitigate mutual interference with other systems and improve target detection performance while also maintaining sufficient utilization of the available frequency bands required for a fine range resolution. We demonstrate that our approach, based on the Deep Q-Learning (DQL) algorithm, enhances important radar metrics, including SINR and bandwidth utilization, more effectively than policy iteration or sense-and-avoid (SAA) approaches in a variety of realistic coexistence environments. We also extend the DQL-based approach to incorporate Double Q-learning and a recurrent neural network to form a Double Deep Recurrent Q-Network (DDRQN). We demonstrate the DDRQN results in favorable performance and stability compared to DQL and policy iteration. Finally, we demonstrate the practicality of our proposed approach through a discussion of experiments performed on a software defined radar (SDRadar) prototype system. Our experimental results indicate that the proposed Deep RL approach significantly improves radar detection performance in congested spectral environments when compared to policy iteration and SAA.