使用多普勒雷达探测和分类地面目标

Cherchour Foued, M. Ammar, Younsi Arezki
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引用次数: 2

摘要

本文主要研究了地面运动目标的检测与分类问题。我们在时频域应用了不同已知的恒虚警率(CFAR)检测器。首先采用简单的短时傅里叶变换(STFT)对微多普勒信号进行频域变换,然后根据微多普勒信号的特性,引入了高分辨率的多信号分类方法(MUSIC)。所研究的检测器有:细胞平均检测器(CA)、SO、GO、OS和CMLD CFAR。对于第二个问题,我们使用了五个阶段的分层分类。在每个阶段,我们使用基于GMM(高斯混合模型)、MFCC (Mel-frequency Cepstral Coefficients)、ESPRIT提取的优势频率或微多普勒步态特征调制的分类方法。从多普勒雷达获得μD信号数据库,得到的结果是可以接受的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection and classification of ground targets using a Doppler RADAR
In this article, we address the problems of detection and classification of ground moving targets. We have applied different known Constant False Alarm Rate (CFAR) detectors in the Time-Frequency domain. The transformation to the frequency domain is done first by a simple short time Fourier transformation (STFT) then, due to the micro-Doppler signal properties, we have introduced the high resolution spectral method which is MUltiple SIgnal Classification (MUSIC). The studied detectors are: cell averaged (CA), SO, GO, OS and CMLD CFAR. With the second problem, we applied the hierarchical classification using five stages. We used in each stage a method of classification based either on GMM (Gaussian Mixture Model), MFCC (Mel-frequency Cepstral Coefficients), the dominant frequencies extracted by ESPRIT or the modulation of the micro-Doppler gait signature. The data base of μD signals is obtained from a Doppler radar and the obtained results are acceptable.
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