Disentangled Representation Learning for Robust Radar Inter-Pulse Modulation Feature Extraction and Recognition

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 19; p. 3585
• Main Authors: Zhang, Luyao, Zhu, Mengtao, Zhang, Ziwei, Li, Yunjie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Analysis; Artificial intelligence; Datasets; disentangled neural network; Electromagnetism; Electronic warfare; Environmental degradation; Feature extraction; generative model; inter-pulse modulation recognition; Learning; Learning theory; Machine learning; Neural networks; Noise measurement; Parameters; Pattern recognition; Performance degradation; Pulse modulation; Radar; Radar systems; Radar transmission; representation learning; Representations; robust feature extraction; Robustness; Sequences; Variables
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16193585

Modern Multi-Function Radars (MFRs) are sophisticated sensors that are capable of flexibly adapting their control parameters in transmitted pulse sequences. In complex electromagnetic environments, efficiently and accurately recognizing the inter-pulse modulations of non-cooperative radar pulse sequences is a key step for modern Electronic Support (ES) systems. Existing recognition methods focus more on algorithmic designs, such as neural network structure designs, to improve recognition performance. However, in open electromagnetic environments with increased flexibility in radar transmission, these methods would suffer performance degradation due to domain shifts between training and testing datasets. To address this issue, this study proposes a robust radar inter-pulse modulation feature extraction and recognition method based on disentangled representation learning. At first, inspired by the Representation Learning Theory (RLT), the received radar pulse sequences can be disentangled into three explanatory factors related to (i) modulation types, (ii) modulation parameters, and (iii) measurement characteristics, such as measurement noise. Then, an explainable radar pulse sequence disentanglement network is proposed based on auto-encoding variational Bayes. The features extracted through the proposed method can effectively represent the key latent factors related to recognition tasks and maintain performance under domain shift conditions. Experiments on both ideal and non-ideal situations demonstrate the effectiveness, robustness, and superiority of the proposed method in comparison with other methods.