A sparse probabilistic approach with chaotic artificial bee colony optimization for sea clutter soft computing

Sun, Yuanmeng; Liu, Xinggao; Zhang, Zeyin; Wang, Zhicheng; Yu, Yu-Sheng; Zhang, Tianjian; Zhu, Yu; Song, Zhengji

doi:10.1016/j.asoc.2016.08.010

Cited by 4 publications

(1 citation statement)

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“…RVM builds a learning machine based on the Bayesian framework (Karimi and McAuley, 2016), the relevance vectors in RVM are more sparse than the support vectors in the support vector machine, which results in shorter training and testing time and is more suitable for online applications (Sun et al, 2017; Wong et al, 2011). RVM has good results in many fields (Jiang et al, 2012; Sun et al, 2016; Zhang et al, 2019a). However, it has few applications in the field of radar operating modes identification.…”

Section: Methodsmentioning

confidence: 99%

A novel radar operating mode identification approach based on variational relevance vector machine with chaotic gravitational search optimization

Chen

Wang

Zou³

et al. 2022

Transactions of the Institute of Measurement and Control

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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.

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