Automatic Data-Driven Frequency-Warped Cepstral Feature Design for Micro-Doppler Classification
Automatic Data-Driven Frequency-Warped Cepstral Feature Design for Micro-Doppler Classification
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DOI：
10.1109/taes.2018.2801378
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发表时间：
2018-02
影响因子：
4.4
通讯作者：
B. Erol;M. Amin;S. Gurbuz
中科院分区：
文献类型：
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作者：
B. Erol;M. Amin;S. Gurbuz
微多普勒特征分析和语音处理具有共同的方法，因为两者都依赖于从信号的时频分布中提取特征来进行分类。因此，诸如Mel频率倒谱系数(MFCC)的特征被提出用于微多普勒分类，这些特征在语音处理中已经显示出成功。MFCC最初的设计是考虑到人耳的听觉特性，通过使用根据MEL频率尺度间隔的滤波器组对信号进行滤波。然而，雷达微多普勒背后的物理基础与人类的听觉或言语无关。这项工作表明，梅尔尺度滤波器组导致了对雷达微多普勒信号分类有重要意义的频率分量的损失。提出了一种新的频率扭曲倒谱特征设计方法，作为一种以数据驱动的方式优化特征有效性的手段，特别适用于微多普勒分析。在四类和八类人类活动分类问题的模拟和测量数据集上，所提出的频率扭曲倒谱系数的性能优于MFCC。
Micro-Doppler signature analysis and speech processing share a common approach as both rely on the extraction of features from the signal's time-frequency distribution for classification. As a result, features, such as the mel-frequency cepstrum coefficients (MFCCs), which have shown success in speech processing, have been proposed for use in micro-Doppler classification. MFCCs were originally designed to take into account the auditory properties of the human ear by filtering the signal using a filter bank spaced according to the mel-frequency scale. However, the physics underlying radar micro-Doppler is unrelated to that of human hearing or speech. This work shows that the mel-scale filter bank results in the loss of frequency components significant to the classification of radar micro-Doppler. A novel method for frequency-warped cepstral feature design is proposed as a means for optimizing the efficacy of features in a data-driven fashion specifically for micro-Doppler analysis. It is shown that the performance of the proposed frequency warped cepstral coefficients outperforms MFCC based on both simulated and measured data sets for four-class and eight-class human activity classification problems.