Online Bayesian Meta-Learning for Cognitive Tracking Radar

TL;DR

This paper introduces an online meta-learning method for cognitive radar that leverages prior tracking experiences to improve waveform selection efficiency across diverse environments, enhancing adaptability and performance.

Contribution

It develops a novel online meta-learning framework for waveform-agile tracking, utilizing Bayesian methods and PAC-Bayes bounds to improve learning speed and accuracy across different scenes.

Findings

Meta-learning accelerates learning in new tracking scenes.

Bayesian bounds quantify performance improvements.

Simulation shows enhanced tracking performance with the method.

Abstract

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.