基于HRRP时谱特征和多尺度非对称卷积神经网络的目标识别算法

Q3 Engineering
Tao Yun, Q. Pan, Yuhang Hao, Rong Xu
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引用次数: 0

摘要

针对空间目标识别中特征提取困难、精度低的问题,提出了一种基于时间谱图特征和多尺度卷积神经网络的雷达HRRP识别算法。首先,利用归一化消除强度灵敏度,利用多个主散射体的绝对对准消除平移灵敏度,利用雷达多普勒速度消除目标高速运动对HRRP的加宽效应、畸变和波峰分裂。然后,该方法对预处理后的HRRP进行时频分析,提取时频图。最后,通过不同尺度的非对称卷积,提取出不同精细尺度、不同方向的时频特征。数据处理结果表明,该方法具有较高的目标识别精度。此外,本发明在同一平台上提高了反姿态灵敏度和目标识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Target recognition algorithm based on HRRP time-spectrogram feature and multi-scale asymmetric convolutional neural network
A radar HRRP recognition algorithm based on time-spectrogram feature and multi-scale convolutional neural network is proposed to address the difficult feature extraction and low accuracy in space target recognition. Firstly, the normalization is used to eliminate the intensity sensitivity, the absolute alignment of multiple dominant scatterers is used to eliminate the translation sensitivity, and the radar Doppler velocity is used to eliminate the widening effect, distortion and wave crest splitting on HRRP caused by high-speed motion of the target. Then, the method applies the time-frequency analysis to the preprocessed HRRP to extract the time-frequency diagram. Finally, the time-frequency features are extracted with different scales of fineness and different directions through asymmetric convolution of different scales. The data processing results demonstrate that the present method has a high target recognition accuracy. In addition, the present improves the anti-posture sensitivity and target recognition on the same platform.
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来源期刊
西北工业大学学报
西北工业大学学报 Engineering-Engineering (all)
CiteScore
1.30
自引率
0.00%
发文量
6201
审稿时长
12 weeks
期刊介绍:
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