Multivariate Time Series Feature Extraction and Clustering Framework for Multi-Function Radar Work Mode Recognition

• Published in: Electronics (Basel) Vol. 13; no. 8; p. 1412
• Main Authors: Fan, Ruozhou, Zhu, Mengtao, Zhang, Xiongkui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Algorithms; Classification; Clustering; Datasets; Deep learning; Employee motivation; Feature extraction; Flexibility; Machine learning; Multivariate analysis; Neural networks; Parameters; Performance degradation; Purity; Radar; Radar systems; Sequences; Time series; Unsupervised learning; Waveforms

Multi-Function Radars (MFRs) are sophisticated sensors with great agility and flexibility in adapting their transmitted waveform and control parameters. The recognition of MFR work modes based on the intercepted pulse sequences plays an important role in interpreting the functional purpose and threa...