# Online Bayesian Meta-Learning for Cognitive Tracking Radar

arXiv: https://arxiv.org/abs/2207.06917 · HTML: https://mlsanity.com/paper/2207.06917/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 87 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 18 of 18)

- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Online Meta-Learning](https://mlsanity.com/paper/1902.08438/) (2019)
- [Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory](https://mlsanity.com/paper/1711.01244/) (2017)
- [Meta-Thompson Sampling](https://mlsanity.com/paper/2102.06129/) (2021)
- [Bayesian decision-making under misspecified priors with applications to meta-learning](https://mlsanity.com/paper/2107.01509/) (2021)
- [Thompson Sampling for Complex Bandit Problems](https://mlsanity.com/paper/1311.0466/) (2013)
- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020)
- [Prior-free and prior-dependent regret bounds for Thompson Sampling](https://mlsanity.com/paper/1304.5758/) (2013)
- [Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning](https://mlsanity.com/paper/1908.09049/) (2019)
- [From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems](https://mlsanity.com/paper/2001.01227/) (2020)
- [Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments](https://mlsanity.com/paper/2006.13173/) (2020)
- [Transfer Meta-Learning: Information-Theoretic Bounds and Information Meta-Risk Minimization](https://mlsanity.com/paper/2011.02872/) (2020)
- [Toward Millimeter Wave Joint Radar-Communications: A Signal Processing Perspective](https://mlsanity.com/paper/1905.00690/) (2019)
- [Prediction of mmWave/THz Link Blockages through Meta-Learning and Recurrent Neural Networks](https://mlsanity.com/paper/2106.07442/) (2021)
- [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)
- [Fast Data-Driven Adaptation of Radar Detection via Meta-Learning](https://mlsanity.com/paper/2112.01780/) (2021)
- [Universal Learning Waveform Selection Strategies for Adaptive Target Tracking](https://mlsanity.com/paper/2202.05294/) (2022)

## Abstract (arXiv)

A key component of cognitive radar is the ability to generalize, or achieve consistent performance across a range of sensing environments, since aspects of the physical scene may vary over time. This presents a challenge for learning-based waveform selection approaches, since transmission policies which are effective in one scene may be highly suboptimal in another. We address this problem by strategically biasing a learning algorithm by exploiting high-level structure across tracking instances, referred to as meta-learning. In this work, we develop an online meta-learning approach for waveform-agile tracking. This approach uses information gained from previous target tracks to speed up and enhance learning in new tracking instances. This results in sample-efficient learning across a class of finite state target channels by exploiting inherent similarity across tracking scenes, attributed to common physical elements such as target type or clutter statistics. We formulate the online waveform selection problem within the framework of Bayesian learning, and provide prior-dependent performance bounds for the meta-learning problem using Probability Approximately Correct (PAC)-Bayes theory. We present a computationally feasible meta-posterior sampling algorithm and study the performance in a simulation study consisting of diverse scenes. Finally, we examine the potential performance benefits and practical challenges associated with online meta-learning for waveform-agile tracking.