A novel radar operating mode identification approach based on variational relevance vector machine with chaotic gravitational search optimization. (May 2023)

• Record Type: Journal Article
• Authors: Chen, Xinjie; Wang, Wenhai; Zou, Ruping; Zhang, Zeyin; Chen, Shichao; Wang, Jun; Liu, Xinggao
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 45:Number 8(2023)
• Page Start: 1397
• Page End: 1410
• Publication Date: 2023-05
• Subjects: Radar operating mode identification -- gravity search algorithm -- variational relevance vector machine -- ensemble algorithm; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/01423312221134145
• Languages: English
• ISSNs: 0142-3312

Abstract

Radar operating mode identification is one of the top priorities in the electronic countermeasure field for threat prediction. However, the inference speed and accuracy of existing identification methods are still not satisfactory for threat detection, and some of them need extra prior knowledge to extract features. This paper introduces an identification approach for radar operating mode to solve these problems. The variational relevance vector machine (VRVM) is presented as the feature extractor and the basic unit of identification. An improved chaotic gravity search algorithm (CGSA) is developed to increase the search breadth and optimize the hyperparameters of VRVM. Threshold-one-versus-one (TOVO) is further proposed to screen the identification results to form the final ensemble method called CGSA–VRVM–TOVO. Experimental results indicate the CGSA–VRVM–TOVO achieves 99.44% accuracy without prior knowledge. Compared with XGBoost, Random Forest, and LightGBM, our approach is 1.94%, 0.55%, and 1.11% higher in accuracy, respectively, and twice as fast as the fastest method of them all.