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Genetic algorithm for rigid body reconstruction after micro-Doppler removal in the radar imaging analysis

机译:雷达成像分析中去除微多普勒后的刚体重建遗传算法

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摘要

Recently, an L-statistics based method for the micro-Doppler effects removal has been proposed by the authors. Order statistics is performed on the spectrogram, while the rigid body signal synthesis is done by using the remaining STFT samples, after micro-Doppler removal. By the proposed method, the Fourier transform is recovered with a concentration close to the original one. However, during the procedure of the micro-Doppler removal, the STFT samples that correspond to the rigid body are retracted, as well. Consequently, in the reconstructed Fourier transform of the rigid body, we get one very highly concentrated pulse, as in the original Fourier transform, and a number of low-concentrated components, being spread around the peak. These low concentrated components are summed up by different random phases. In this paper, we propose a genetic algorithm for the estimation of the removed STFT samples corresponding to the rigid body. Each individual in the genetic algorithm contains possible estimation of the phases of the missing STFT samples, whereas fitness function forces individuals (combination of phases) for which a minimal energy of the side lobs is obtained. The individual with the highest fitness is considered as the final phases' estimation of the missing STFT values, and then used for the reconstruction of the original Fourier transform. The amplitude of a STFT sample is estimated as median of the amplitudes of the remaining samples at the same frequency. Performance of the proposed genetic algorithm is illustrated by examples.
机译:最近,作者提出了一种基于L统计量的微多普勒效应消除方法。在去除多普勒仪之后,对频谱图执行阶次统计,而通过使用剩余的STFT样本完成刚体信号合成。通过提出的方法,傅立叶变换以接近原始的浓度被恢复。但是,在微多普勒仪去除过程中,对应于刚体的STFT样本也会缩回。因此,在刚体的重构傅立叶变换中,我们得到了一个非常高的脉冲,就像原始的傅立叶变换一样,还有许多低浓度的分量在峰周围扩散。这些低浓度的成分由不同的随机相位相加。在本文中,我们提出了一种遗传算法,用于估计与刚体相对应的STFT样本。遗传算法中的每个个体都包含丢失的STFT样本的相位的可能估计,而适应度函数会迫使个体(相位的组合)获得最小的旁瓣能量。适合度最高的个人被视为丢失STFT值的最终阶段估计,然后用于原始傅立叶变换的重建。 STFT样本的幅度估计为相同频率下其余样本的幅度的中值。实例说明了所提出的遗传算法的性能。

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