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Micro-Doppler Classification for Ground Surveillance Radar Using Speech Recognition Tools

机译:利用语音识别工具对地面监视雷达进行微多普勒分类

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Among the applications of a radar system, target classification for ground surveillance is one of the most widely used. This paper deals with micro-Doppler Signature (μ-DS) based radar Automatic Target Recognition (ATR). The main goal for performing μ-DS classification using speech processing tools was to investigate whether automatic speech recognition (ASR) techniques are suitable methods for radar ATR. In this work, extracted features from micro-Doppler echoes signal, using MFCC, LPC and LPCC, are used to estimate models for target classification. In classification stage, two parametric models based on Gaussian Mixture Model (GMM) and Greedy GMM were successively investigated for echo target modeling. Maximum a posteriori (MAP) and Majority-voting post-processing (MV) decision schemes are applied. Thus, ASR techniques based on GMM and GMM Greedy classifiers have been successfully used to distinguish different classes of targets echoes (humans, truck, vehicle and clutter) recorded by a low-resolution ground surveillance Doppler radar. Experimental results show that MV post processing improves target recognition and the performances reach to 99,08% correct classification on the testing set.
机译:在雷达系统的应用中,地面监视的目标分类是使用最广泛的目标之一。本文讨论了基于微多普勒签名(μ-DS)的雷达自动目标识别(ATR)。使用语音处理工具执行μ-DS分类的主要目的是研究自动语音识别(ASR)技术是否适合雷达ATR。在这项工作中,使用MFCC,LPC和LPCC从微多普勒回波信号中提取的特征用于估计目标分类的模型。在分类阶段,相继研究了基于高斯混合模型(GMM)和贪婪GMM的两个参数模型进行回波目标建模。应用了最大后验(MAP)和多数表决后处理(MV)决策方案。因此,基于GMM和GMM Greedy分类器的ASR技术已成功用于区分由低分辨率地面监视多普勒雷达记录的目标回波的不同类别(人,卡车,车辆和杂波)。实验结果表明,MV后处理提高了目标识别率,在测试集上的性能达到正确分类的99.08%。

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