Towards Radar Emitter Recognition in Changing Environments with Domain Generalization

TL;DR

This paper introduces a domain generalization framework for radar emitter recognition that enhances adaptability in changing environments by using noise generators and an adversarial scene classifier, improving recognition robustness.

Contribution

The paper presents a novel domain generalization approach with specialized noise generators and an adversarial classifier to improve radar signal recognition across diverse environments.

Findings

Enhanced recognition accuracy in varied environments

Effective simulation of diverse scenes with noise generators

Superior performance demonstrated through experiments

Abstract

Analyzing radar signals from complex Electronic Warfare (EW) environment is a non-trivial task.However, in the real world, the changing EW environment results in inconsistent signal distribution, such as the pulse repetition interval (PRI) mismatch between different detected scenes.In this paper, we propose a novel domain generalization framework to improve the adaptability of signal recognition in changing environments.Specifically, we first design several noise generators to simulate varied scenes. Different from conventional augmentation methods, our introduced generators carefully enhance the diversity of the detected signals and meanwhile maintain the semantic features of the signals. Moreover, we propose a signal scene domain classifier that works in the manner of adversarial learning. The proposed classifier guarantees the signal predictor to generalize to different scenes. Extensive comparative experiments prove the proposed method's superiority.