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A Radar Target Recognition Method Based on Circular Convolution Coefficients of High-Resolution Range Profiles

机译:基于高分辨率范围型材循环卷积系数的雷达目标识别方法

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In order to extract robust features for radar high-resolution range profile (HRRP) recognition, a feature extraction method using circular convolution coefficients of HRRP is proposed in this paper. The features extracted by the proposed method represent the HRRP structure information which is more robust compared with local details. Moreover, this feature can make up for the loss of HRRP local details caused by feature selection. Due to the high dimensionality and nonlinear separability of HRRP features, feature selection based on kernel class separability was also used. Experiment results on simulation HRRP data sets are analyzed to demonstrate the efficiency of our method.
机译:为了提取雷达高分辨率范围(HRRP)识别的鲁棒特征,本文提出了使用HRRP圆形卷积系数的特征提取方法。由所提出的方法提取的特征表示与本地细节相比的更稳健的HRRP结构信息。此外,此功能可以弥补由特征选择引起的HRRP本地细节的丢失。由于HRRP特征的高维度和非线性可分离性,还使用了基于内核级别可分离的特征选择。分析了模拟HRRP数据集的实验结果,展示了我们方法的效率。

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