Open AccessRecognition method of dense false targets jamming based on time‐frequency atomic decomposition
Author(s) -
Hao Zhimei,
Yu Wen,
Chen Wei
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0147
Subject(s) - jamming , pattern recognition (psychology) , computer science , time–frequency analysis , artificial intelligence , gabor transform , signal (programming language) , feature (linguistics) , radar , support vector machine , ambiguity function , ambiguity , feature vector , speech recognition , physics , waveform , telecommunications , programming language , linguistics , philosophy , thermodynamics
Dense false target jamming can affect radar detection performance severely. A method of dense false targets jamming recognition based on time‐frequency atomic decomposition theory and support vector machine (SVM) is proposed to solve the difficulty of dense false targets jamming identification. According to the feature of ambiguity function of dense false targets jamming signal, a Gabor sub‐dictionary which has adaptive variation with signal is designed. The signal is expanded into the corresponding Gabor time‐frequency dictionary by sparse decomposition. After Gabor atomic time‐frequency parameters are extracted as individual feature vectors, SVM is utilised for classification and recognition. The experimental results show that the extracted Gabor atomic time‐frequency parameters can effectively represent the essential features of target and dense false targets jamming, respectively, and this method has a high recognition rate.