Target Identification of Acoustic Signals Based on Multifractal Analysis and Support Vector Machine

doi:10.3969/j.issn.1000-1093.2012.12.019

Abstract

Abstract: In order to improve the recognition rate of smart landmines for armored target，as the acoustic signals radiated from armored vehicles have been proved to be nonlinear，an identification model based on multifractal analysis and support vector machine(SVM) was established．40 sample signals for each armored target(a certain type of wheeled armored vehicle and a tank) running in different speeds(2 working conditions) were collected by outdoor experiment．The generalized fractal dimension spectrums(GFDS) for both target signals were calculated based on multifractal analysis，and the characters of GFDS under 2 working conditions were analyzed．The GFDS values were input into SVM classification model，and the optimal identification results were obtained by training the model．After an identification effect comparison between GFDS and wavelet packet energy (WPE) method，the results show that the model based on GFDS and SVM has a recognition rate of 92.5%，which is higher than the 85% by WPE method．

CLC Number:

TJ4
TN911.7

DING Kai， FANG Xiang， ZHANG Wei-ping， FAN Lei， LI Xing-hua， XIE Li-jun. Target Identification of Acoustic Signals Based on Multifractal Analysis and Support Vector Machine[J]. Acta Armamentarii, 2012, 33(12): 1521-1526.

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