Searching Efficient Deep Architectures for Radar Target Detection using Monte-Carlo Tree Search

TL;DR

This paper introduces a Monte-Carlo Tree Search-based neural architecture search method to find efficient deep neural networks for radar target detection, balancing detection performance and computational complexity in cluttered environments.

Contribution

It proposes a novel NAS approach using MCTS to discover lightweight neural networks that maintain high detection accuracy in radar applications.

Findings

Discovered neural architectures that outperform baseline models in detection probability.

Achieved significant reduction in model complexity while maintaining performance.

Validated generalization on endoclutter radar signals.

Abstract

Recent research works establish deep neural networks as high performing tools for radar target detection, especially on challenging environments (presence of clutter or interferences, multi-target scenarii...). However, the usually large computational complexity of these networks is one of the factors preventing them from being widely implemented in embedded radar systems. We propose to investigate novel neural architecture search (NAS) methods, based on Monte-Carlo Tree Search (MCTS), for finding neural networks achieving the required detection performance and striving towards a lower computational complexity. We evaluate the searched architectures on endoclutter radar signals, in order to compare their respective performance metrics and generalization properties. A novel network satisfying the required detection probability while being significantly lighter than the expert-designed baseline is proposed.