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Recurrent neural network for high-resolution radar ship target recognition

机译:递归神经网络用于高分辨率雷达舰船目标识别

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The high-resolution radar waveform describes the amplitude of targets' multiple scattering centers and their distribution in the radial axis. As viewed from the time domain, the target waveform can also be regarded as a time sequence such that it can be classified using recurrent neural networks (RNN) which are suitable for time sequence processing. A modified partially RNN and its algorithm are proposed. This method reaches an average recognition rate of above 90% for 8 class high-resolution radar targets, and it is tolerant of time shift to a certain degree.
机译:高分辨率雷达波形描述了目标的多个散射中心的幅度及其在径向轴上的分布。从时域上看,目标波形也可以视为时间序列,以便可以使用适用于时间序列处理的递归神经网络(RNN)对其进行分类。提出了一种改进的部分RNN及其算法。该方法对8类高分辨率雷达目标的平均识别率达到90%以上,并且在一定程度上可以容忍时间偏移。

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