| 摘要: |
| 针对低信噪比下雷达目标一维距离像质量不高、影响目标识别率的问题，将小波阈值降噪的方法应用到雷达目标一维距离像识别研究中，设计了一种新的小波阈值函数，提出了基于小波阈值降噪的雷达一维距离像识别的方法。利用仿真数据进行实验验证，以LVQ（Learning Vector Quantization）神经网络作为分类识别器，进行目标的分类识别研究。结果表明，将小波阈值降噪用于雷达目标一维距离像识别，在低信噪比时能够有效地降低噪声，提高距离像的质量，从而提高目标一维距离像识别率，同时实验也验证了所提出的新阈值函数相较于原阈值函数能更加有效地降低噪声，提高识别率。 |
| 关键词: 小波 阈值函数 一维距离像 目标识别 LVQ神经网络 |
| DOI：DOI:10.3969/j.issn.1672-2337.2019.04.002 |
| 分类号:TN957 |
| 基金项目:国家自然科学基金(No.61471379,61790551,61102166)；国防科技项目基金(No.2102028)；装备发展部“十三五”预研项目 (No.41413060101)；泰山学者工程专项经费资助项目 |
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| Radar One-Dimensional Range Profile Recognition Based on Improved Wavelet Denoising |
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GUO Xiaokang，JIAN Tao，DONG Yunlong
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Research Institute of Information Fusion，Naval Aviation University，Yantai 264001，China
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| Abstract: |
| For the problem that low one-dimensional range profile quality of radar target has affect on the target recognition rate at low SNR，a new threshold function is designed and a method of radar one-dimensional range profile recognition based on wavelet threshold denoising is proposed. The simulation data is used to make experimental verification. The LVQ neural network is used as the classification recognizer to carry out the target classification and recognition. The results show that the wavelet threshold denoising can effectively reduce the noise and improve the range profile quality at low SNR so as to improve the target one-dimensional range profile recognition rate. The experiment also verifies that the proposed new threshold function can reduce the noise and improve the recognition rate more effectively than the original threshold function. |
| Key words: wavelet threshold function one dimensional range profile target recognition LVQ neural network |
