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

TL;DR

This paper introduces a universal learning approach using a context-tree model for waveform selection in radar tracking, adapting to target channels with memory and improving performance over existing methods.

Contribution

It presents a novel context-tree based learning algorithm that asymptotically converges to the optimal waveform selection policy in Markovian target channels.

Findings

Improved tracking performance in simulations compared to state-of-the-art schemes.

Guarantees convergence to the optimal policy for stationary Markov channels.

Effective adaptation to dynamic and adversarial target environments.

Abstract

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 < $\infty$, 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.