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A Micro-Doppler Frequency Ambiguity Resolution Method Based on Complex-Valued U-Net

机译:一种基于复合U-Net的微多普勒频率模糊分辨率

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In order to resolute the micro-Doppler frequency ambiguity caused by radar pulse repetition frequency not high enough (i.e., pulse dimension does not satisfy the requirement of Nyquist sampling theorem), this paper presents a micro-Doppler frequency ambiguity resolution method based on complex-valued U-net. The echo sequence is interpolated by zeros in the pulse dimension to increase the equivalent pulse repetition frequency, so that the echo sequence after zero interpolation contains the real micro-Doppler frequency; at the same time, some new frequency components are generated. The variation law of the echo sequence frequency after zero interpolation is analyzed. Then, the echo sequence in time domain after zero interpolation is transformed to the time-frequency domain by short-time Fourier transform (STFT). Finally, the time-frequency results can be segmented by the model, which is trained by complex-valued U-net to eliminate the redundant frequencies generated by zero interpolation; thus, the reconstruction of real micro-Doppler frequency is realized. Theoretical analysis and simulation results show that the proposed method can solve the problem of micro-Doppler frequency ambiguity. Compared with fully convolution network (FCN) and fully convolution residual network (FCRN), the proposed method has better performance and robustness.
机译:为了解决因雷达脉冲重复频率而不是足够高的微多普勒频率模糊(即,脉冲尺寸不满足奈奎斯特采样定理的要求),本文提出了一种基于复杂的微多普勒频率模糊分辨率解析方法有价值的U-net。回声序列通过脉冲尺寸中的零内插,以增加等效的脉冲重复频率,使得零插值后的回波序列包含真实的微多普勒频率;同时,生成一些新的频率分量。分析了零插值后回波序列频率的变化定律。然后,通过短时间傅里叶变换(STFT)将零插值变换为时域的时域中的回波序列。最后,模型可以分割时频结果,该模型由复值U-Net训练,以消除零插值产生的冗余频率;因此,实现了真实微多普勒频率的重建。理论分析和仿真结果表明,该方法可以解决微多普勒频率模糊的问题。与完全卷积网络(FCN)和完全卷积剩余网络(FCRN)相比,所提出的方法具有更好的性能和鲁棒性。

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