Automatic recognition of sonar targets using feature selection in micro-Doppler signature

作     者：Abbas Saffari Seyed-Hamid Zahiri Mohammad Khishe Abbas Saffari;Seyed-Hamid Zahiri;Mohammad Khishe

作者机构：Department of Electrical EngineeringUniversity of BirjandBirjandIran Department of Electrical EngineeringImam Khomeini Marine Science UniversityNowshahrIran 

出 版 物：《Defence Technology（防务技术）》 (Defence Technology)

年 卷 期：2023年第20卷第2期

页      面：58-71页

核心收录：

学科分类：082403[工学-水声工程] 08[工学] 0824[工学-船舶与海洋工程] 

主　　题：Micro-Doppler signature Automatic recognition Feature selection k-NN PSO 

摘      要：Currently,the use of intelligent systems for the automatic recognition of targets in the fields of defence and military has increased *** primary advantage of these systems is that they do not need human participation in target recognition *** paper uses the particle swarm optimization(PSO)algorithm to select the optimal features in the micro-Doppler signature of sonar *** microDoppler effect is referred to amplitude/phase modulation on the received signal by rotating parts of a target such as *** different targets geometric and physical properties are not the same,their micro-Doppler signature is *** Inconsistency can be considered a practical issue(especially in the frequency domain)for sonar target *** using 128-point fast Fourier transform(FFT)for the feature extraction step,not all extracted features contain helpful *** a result,PSO selects the most optimum and valuable *** evaluate the micro-Doppler signature of sonar targets and the effect of feature selection on sonar target recognition,the simplest and most popular machine learning algorithm,k-nearest neighbor(k-NN),is used,which is called k-PSO in this paper because of the use of PSO for feature *** parameters measured are the correct recognition rate,reliability rate,and processing *** simulation results show that k-PSO achieved a 100%correct recognition rate and reliability rate at 19.35 s when using simulated data at a 15 dB signal-tonoise ratio(SNR)angle of 40°.Also,for the experimental dataset obtained from the cavitation tunnel,the correct recognition rate is 98.26%,and the reliability rate is 99.69%at ***,the k-PSO has an encouraging performance in automatically recognizing sonar targets when using experimental datasets and for real-world use.