Intelligent target recognition based on wavelet packet neural network
Intelligent target recognition based on wavelet packet neural network
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DOI：
10.1016/j.eswa.2005.01.016
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发表时间：
2005-07-01
影响因子：
8.5
通讯作者：
Poyraz, M
中科院分区：
文献类型：
--
作者：
Avci, E;Turkoglu, I;Poyraz, M
本文提出了一种智能目标识别系统。高分辨距离雷达目标回波信号的识别。本文重点研究了X波段脉冲雷达实测真实的目标回波信号波形的特征提取与分类相结合的方法。基于此，本文采用了我们开发的小波包神经网络模型。该模型由两层组成：小波和多层感知器。小波层由小波包分解和小波熵组成，用于时频域的自适应特征提取。用于分类的多层感知器是一个前馈神经网络。已开发的系统的性能进行了评估，在嘈杂的雷达目标回波（RTE）信号。测试结果表明，该系统对检测真实的RTE信号是有效的。对所用目标对象的正确分类率约为95%。(c)2005爱思唯尔有限公司保留所有权利。
In this paper, an intelligent target recognition system is presented for target. recognition from target echo signal of High Resolution Range (HRR) radars. This paper especially deals with combination of the feature extraction and classification from measured real target echo signal waveforms using X-band pulse radar. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in noisy radar target echo (RTE) signals. The test results showed that this system was effective in detecting real RTE signals. The correct classification rate was about 95% for used target subjects. (c) 2005 Elsevier Ltd. All rights reserved.