Model-Based Time Series Clustering and Interpulse Modulation Parameter Estimation of Multifunction Radar Pulse Sequences

• Published in: IEEE transactions on aerospace and electronic systems Vol. 57; no. 6; pp. 3673 - 3690
• Main Authors: Zhu, Mengtao, Li, Yunjie, Wang, Shafei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering; Clustering algorithms; Clustering methods; Interpulse modulation; Mathematical models; Modulation; multifunction radars; Parameter estimation; Parametric statistics; pattern recognition; Pulse repetition interval; Radar; Recognition; Sequences; Task analysis; Time series; Time series analysis; time series clustering
• ISSN: 0018-9251; 1557-9603
• DOI: 10.1109/TAES.2021.3082660

Multifunction radars (MFRs) are sophisticated sensors with significant intelligence, flexibility, and agility. It is important to analyze the system behavior and interpret the intentions of an MFR through the recognition of consecutive work modes in a pulse sequence. With the rapid development of MFRs, the agility capabilities in work mode modulations and the modulation parameters have increased, making the recognition task more challenging. Therefore, it is essential to develop new methods that are more adaptive and less depended on prior information. This article proposes a new method for the time series clustering of MFR work modes named as model-based radar time series clustering. The proposed method considers the variable modulation parameters of submode and employs three algorithms for different assumptions of progressive availability of the priors of a noncooperative MFR. The experiments with four typical pulse repetition interval modulations simulation samples validate the feasibility and the superior performance of the proposed method over the state-of-the-art pulse sequence clustering methods for recognition of MFR work modes.