# Universal Learning Waveform Selection Strategies for Adaptive Target Tracking

arXiv: https://arxiv.org/abs/2202.05294 · HTML: https://mlsanity.com/paper/2202.05294/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 64 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [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)
- [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)
- [Spatio-temporal Interference Correlation and Joint Coverage in Cellular Networks](https://mlsanity.com/paper/1606.05332/) (2016)
- [Waveform Selection for Radar Tracking in Target Channels With Memory via Universal Learning](https://mlsanity.com/paper/2108.01181/) (2021)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Online selection of optimal waveforms for target tracking with active sensors has long been a problem of interest. Many conventional solutions utilize an estimation-theoretic interpretation, in which a waveform-specific Cram'er-Rao lower bound on measurement error is used to select the optimal waveform for each tracking step. However, this approach is only valid in the high SNR regime, and requires a rather restrictive set of assumptions regarding the target motion and measurement models. Further, due to computational concerns, many traditional approaches are limited to near-term, or myopic, optimization, even though radar scenes exhibit strong temporal correlation. More recently, reinforcement learning has been proposed for waveform selection, in which the problem is framed as a Markov decision process (MDP), allowing for long-term planning. However, a major limitation of reinforcement learning is that the memory length of the underlying Markov process is often unknown for realistic target and channel dynamics, and a more general framework is desirable. This work develops a universal sequential waveform selection scheme which asymptotically achieves Bellman optimality in any radar scene which can be modeled as a Uth order Markov process for a finite, but unknown, integer U. Our approach is based on well-established tools from the field of universal source coding, where a stationary source is parsed into variable length phrases in order to build a context-tree, which is used as a probabalistic model for the scene's behavior. We show that an algorithm based on a multi-alphabet version of the Context-Tree Weighting (CTW) method can be used to optimally solve a broad class of waveform-agile tracking problems while making minimal assumptions about the environment's behavior.