# Waveform Selection for Radar Tracking in Target Channels With Memory via Universal Learning

arXiv: https://arxiv.org/abs/2108.01181 · HTML: https://mlsanity.com/paper/2108.01181/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments](https://mlsanity.com/paper/2006.13173/) (2020)
- [Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning](https://mlsanity.com/paper/2001.02629/) (2020)

## Built on by (most cited first, 1 of 1)

- [Universal Learning Waveform Selection Strategies for Adaptive Target Tracking](https://mlsanity.com/paper/2202.05294/) (2022)

## Abstract (arXiv)

In tracking radar, the sensing environment often varies significantly over a track duration due to the target's trajectory and dynamic interference. Adapting the radar's waveform using partial information about the state of the scene has been shown to provide performance benefits in many practical scenarios. Moreover, radar measurements generally exhibit strong temporal correlation, allowing memory-based learning algorithms to effectively learn waveform selection strategies. This work examines a radar system which builds a compressed model of the radar-environment interface in the form of a context-tree. The radar uses this context tree-based model to select waveforms in a signal-dependent target channel, which may respond adversarially to the radar's strategy. This approach is guaranteed to asymptotically converge to the average-cost optimal policy for any stationary target channel that can be represented as a Markov process of order U < ∞, where the constant U is unknown to the radar. The proposed approach is tested in a simulation study, and is shown to provide tracking performance improvements over two state-of-the-art waveform selection schemes.