# Online Meta-Learning for Scene-Diverse Waveform-Agile Radar Target Tracking

arXiv: https://arxiv.org/abs/2110.11450 · HTML: https://mlsanity.com/paper/2110.11450/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Universal Learning Waveform Selection Strategies for Adaptive Target Tracking](https://mlsanity.com/paper/2202.05294/) (2022)
- [Linear Jamming Bandits: Sample-Efficient Learning for Non-Coherent Digital Jamming](https://mlsanity.com/paper/2207.02365/) (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)

## Abstract (arXiv)

A fundamental problem for waveform-agile radar systems is that the true environment is unknown, and transmission policies which perform well for a particular tracking instance may be sub-optimal for another. Additionally, there is a limited time window for each target track, and the radar must learn an effective strategy from a sequence of measurements in a timely manner. This paper studies a Bayesian meta-learning model for radar waveform selection which seeks to learn an inductive bias to quickly optimize tracking performance across a class of radar scenes. We cast the waveform selection problem in the framework of sequential Bayesian inference, and introduce a contextual bandit variant of the recently proposed meta-Thompson Sampling algorithm, which learns an inductive bias in the form of a prior distribution. Each track is treated as an instance of a contextual bandit learning problem, coming from a task distribution. We show that the meta-learning process results in an appreciably faster learning, resulting in significantly fewer lost tracks than a conventional learning approach equipped with an uninformative prior.