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Intelligent target recognition based on wavelet packet neural network

机译:基于小波包神经网络的智能目标识别

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摘要

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.
机译:本文提出了一种智能目标识别系统,用于从高分辨率测距(HRR)雷达的目标回波信号中识别目标。本文特别讨论了使用X波段脉冲雷达从实测目标回波信号波形中进行特征提取和分类的组合。因此,使用了我们开发的小波包神经网络模型。该模型由两层组成:小波层和多层感知器。小波层用于时频域的自适应特征提取,由小波包分解和小波熵组成。用于分类的多层感知器是前馈神经网络。已在嘈杂的雷达目标回波(RTE)信号中评估了开发系统的性能。测试结果表明,该系统可有效检测实际的RTE信号。对于使用过的目标受试者,正确的分类率约为95%。

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