Automatic target recognition method for inverse synthetic aperture sonar imaging  

作　　者：ZHU Zhaotong PENG Shibao XU Jia XU Xiaomei 

机构地区：[1]The 705th Research Institute, CSIC Xi'an 650011 [2]Department of Electronic Engineering, Tsinghua University Beijing 100084 [3]School of Information and Electronic, Beijing Institute of Technology Beijing 100081 [4]Key Laboratory of Underzvater Acoustic Communication and Marine Information Technology (Xiamen University) Xiamen 361101 [5]School of Ocean and Earth Sciences, Xiamen University Xiamen 361101

出　　处：《Chinese Journal of Acoustics》2018年第4期463-476,共14页声学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(41676024,41376040,41276039,61271391,61671061);the Post-doctor Foundation of Shaanxi Province(2017BSHQYXMZZ04);the Post-doctor Foundation of the 705th Research Institute,CSIC

摘　　要：To address the randomness of target aspect angle and the incompleteness of observed target in inverse synthetic aperture sonar（ISAS） imaging,a method for target recognition is proposed based on topology vector feature（TVF） of multiple highlights. Analysis of the projection relationship from 3 D space to 2 D imaging plane in ISAS indicates that the distance between two highlights in the cross-range scale calibrated image is determined by the distance between the corresponding physical scattering centers. Then, TVFs of different targets, which remain stable in various possibilities of target aspect angle, can be built. K-means clustering technique is used to effectively alleviate effect of the point missing due to incompleteness of the observed target. A nearest neighbor classifier is used to realize the target recognition. The ISAS experimental results using underwater scaled models are provided to demonstrate the effectiveness of the proposed method. A classification rate of 84.0% is reached.To address the randomness of target aspect angle and the incompleteness of observed target in inverse synthetic aperture sonar（ISAS） imaging,a method for target recognition is proposed based on topology vector feature（TVF） of multiple highlights. Analysis of the projection relationship from 3 D space to 2 D imaging plane in ISAS indicates that the distance between two highlights in the cross-range scale calibrated image is determined by the distance between the corresponding physical scattering centers. Then, TVFs of different targets, which remain stable in various possibilities of target aspect angle, can be built. K-means clustering technique is used to effectively alleviate effect of the point missing due to incompleteness of the observed target. A nearest neighbor classifier is used to realize the target recognition. The ISAS experimental results using underwater scaled models are provided to demonstrate the effectiveness of the proposed method. A classification rate of 84.0% is reached.

关 键 词：Automatic target recognition method for inverse synthetic aperture sonar imaging 

分 类 号：TB56[交通运输工程—水声工程]