A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)

摘要

In last year's, the expert target recognition has been become very important topic in radar literature. In this study, a target recognition system is introduced for expert target recognition (ATR) using radar target echo signals of High Range Resolution (HRR) radars. This study includes a combination of an adaptive feature extraction and classification using optimum wavelet entropy parameter values. The features used in this study are extracted from radar target echo signals. Herein, a genetic wavelet extreme learning machine classifier model (GAWELM) is developed for expert target recognition. The CAWELM composes of three stages. These stages of GAWELM are genetic algorithm, wavelet analysis and extreme learning machine (ELM) classifier. In previous studies of radar target recognition have shown that the learning speed of feedforward networks is in general much slower than required and it has been a major disadvantage. There are two important causes. These are: (1) the slow gradient-based learning algorithms are commonly used to train neural networks, and (2) all the parameters of the networks are fixed iteratively by using such learning algorithms. In this paper, a new learning algorithm named extreme learning machine (ELM) for single-hidden layer feedforward networks (SLFNs) Ahem, Delisle, et al., 1989; Al-Otum & Al-Sowayan, 2011; Avci, Turkoglu, & Poyraz, 2005a, 2005b; Biswal, Dash, & Panigrahi, 2009; Frigui et al., in press; Cao, Lin, & Huang, 2010; Guo, Rivero, Dorado, Munteanu, & Pazos, 2011; Famili. Shen, Weber, & Simoudis, 1997; Han & Huang, 2006; Huang, Cai, Chen, & Liu, 2011; Huang, Chen, & Siew, 2006; Huang & Siew, 2005; Huang, Liu, Gao, & Guo, 2009; Jiang, Liu, Li, & Tang, 2011; Kubrusly & Levan, 2009; Le, Tamura, & Matsumoto, 2011; Lhermitte et al., 2011; Martinez-Martinez et al., 2011; Matlab, 2011; Nelson, Starzyk, & Ensley, 2002; Nejad & Zakeri, 2011; Tabib, Sathe, Deshpande, & Joshi, 2009; Tang, Sun, Tang, Zhou, & Wei, 2011, which randomly choose hidden nodes and analytically determines the output weights of SLFNs, to eliminate the these disadvantages of feedforward networks for expert target recognition area. Then, the genetic algorithm (GA) stage is used for obtaining the feature extraction method and finding the optimum wavelet entropy parameter values. Herein, the optimal one of four variant feature extraction methods is obtained by using a genetic algorithm (GA). The four feature extraction methods proposed GAWELM model are discrete wavelet transform (DWT), discrete wavelet transform-short-time Fourier transform (DWT-STFT), discrete wavelet transform-Born-Jordan time-frequency transform (DWT-BJTFT), and discrete wavelet transform-Choi-Williams time-frequency transform (DWT-CWTFT). The discrete wavelet transform stage is performed for optimum feature extraction in the time-frequency domain. The discrete wavelet transform stage includes discrete wavelet transform and calculating of discrete wavelet entropies. The extreme learning machine (ELM) classifier is performed for evaluating the fitness function of the genetic algorithm and classification of radar targets. The performance of the developed GAWELM expert radar target recognition system is examined by using noisy real radar target echo signals. The applications results of the developed GAWELM expert radar target recognition system show that this GAWELM system is effective in rating real radar target echo signals. The correct classification rate of this GAWELM system is about 90% for radar target types used in this study.