基于微多普勒效应的运动车辆目标分类研究
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摘要
微多普勒效应的概念最初在相干激光雷达系统的研究领域中被提出，这种现象可以提供关于运动目标自身特性的描述，可以视为运动目标的独特特征。微多普勒效应自引入雷达领域以来，为雷达目标识别研究提供了新的途径，同时也是现有雷达目标识别方法的有力补充。轮式车辆和履带式车辆在战场中通常承担不同的任务，决定了其威胁程度不同，对轮式和履带式车辆的分类在现代战争中具有重要意义。由于车辆具有典型的微运动结构，在行驶时这些微运动部件会对雷达发射波产生微多普勒调制，因而可以利用微多普勒信息对运动车辆进行分类。此外，对于窄带短驻留时间条件下运动车辆目标分类的研究，有利于解决雷达时间资源分配问题，提升窄带雷达的目标分类能力。
本文主要围绕国防预研等相关项目，紧密结合窄带短驻留时间条件下地面运动目标分类的工程应用背景，从信号预处理、微多普勒信号分析以及目标特征提取、车辆目标分类等方面展开相关理论和技术的研究。论文的主要工作概括如下：
1.对于旋转微运动形式的单散射点以及简单形式目标，介绍了其运动规律和微多普勒信号的数学表达式。在此基础上，针对车辆目标的特点进一步给出了车轮和履带的微多普勒回波信号的数学表达式。并利用车辆回波信号模型分析了实测数据中轮式车辆和履带式车辆的多普勒谱的差异性。分析结果表明，利用车辆目标回波中包含的微多普勒信息可以实现轮式车辆和履带式车辆的分类。
2.针对车辆目标分类的预处理问题，对一些通用的信号预处理方法进行了研究。在杂波抑制方面，指出分类问题中的杂波抑制处理要求对杂波进行消除的同时还需保留信号中微多普勒分量的结构。由此介绍了基于带限CLEAN算法的杂波抑制方法，该方法具有计算量较小，利用单帧回波即能实现的优点。同时，在目标周围杂波性质稳定的假设下，将杂波视为色噪声。由此介绍了基于广义匹配滤波器的杂波抑制方法，该方法对于杂波成分具有自适应性。这两种方法均能在有效抑制杂波成分的同时保留信号多普勒谱的结构，有利于分类信息的保留。在车身速度归一化方面，指出由车身速度变化带来的目标多普勒谱主峰平移和多普勒谱展宽会对分类性能产生影响，由此提出了目标回波信号的车身速度归一化方法，用以消除车身速度的变化对分类结果产生的影响。在微多普勒信息提取方面，分析了车辆回波信号特点的基础上，介绍了目标多普勒谱的非线性变换预处理，用以增强微多普勒成分在分类中的作用。
3.分析了轮式车辆和履带式车辆在多普勒谱形状上的差异性，提出基于目标多普勒谱的车辆分类方法。提取了波形熵，主副峰值比等5种描述多普勒谱形状的特征，并利用这些特征对车辆进行了分类。实验结果表明了方法的有效性。
4.研究了基于能量分布特征的车辆目标分类方法。在分析轮式和履带式车辆回波结构的基础上，指出车辆目标的回波可视为谐波和的形式。提出了基于CLEAN算法谐波分析和信号特征谱的车辆分类方法。这两种方法本质上均利用了轮式和履带式车辆回波中各次谐波成分在能量分布上的差异性。实验结果表明，能量分布特征能够有效区分轮式车辆和履带式车辆。
5.研究了车辆目标分类中，一些能够反映目标结构信息的重要微多普勒分量的利用问题。分析了目标回波中多普勒频率分量之间的相互关系，指出多普勒频率分量之间的相对位置信息反映了目标的结构特点。提出了一种基于经验模态分解（EMD）的车辆目标分层分类结构，该结构在第一层中根据2v分量的存在性初步判别履带式车辆，第二层中则对不存在2v分量的信号进行进一步的分类。该方法提取了目标多普勒谱中反映出的车辆微运动部件的结构信息，并利用这些信息对车辆目标进行分类。此外，由于算法的自适应性，杂波抑制过程也自然的包含于处理过程中。实验结果验证了方法的有效性，说明多普勒分量之间相对位置信息的引入有利于提高分类性能。
6.研究了车辆目标分类方法中的稳健分类问题。指出在窄带短驻留时间条件下，车辆目标分类面临杂波、噪声和低分辨率三个问题。利用压缩感知（CS）的思想，提出了基于回波信号稀疏重构的车辆分类方法，该方法能在抑制杂波和噪声的同时，提高目标多普勒谱的分辨率。在提取微多普勒信息更加准确的基础上，使得分类性能对于信噪比变化具有稳健性。
Micro-Doppler effect was firstly introduced in coherent laser radar system. It containsinformation about the characteristics of interested moving target, and can be seen as a uniquesignature of the target. The research on micro-Doppler provides a new prospect for radarautomatic target recognition (ATR) and it is also a complementary to existing methods.Wheeled and tracked vehicles often undertake different tasks in battle, which determines theyhave different threat degrees, thus the discrimination of the two types of vehicles is importantto a successful tactic. Since vehicles have typical micro motion structures which will inducemicro-doppler modulation on radar signal, micro-Doppler information contained in theirreturned signals can be utilized to discriminant different moving vehicles. Furthermore, fornarrow band radar system, the research of micro-Doppler effect is beneficial to improve theclassification function. In addition, the research in short dwell time condition could help tosolve the contradiction of time resource distribution between various modern radar functions.
This dissertation is supported by Advanced Defense Research Programs of China.Considering the engineering background of the narrow band radar target classification withinshort dwell time, the dissertation studies micro-Doppler signal pre-processing, micro-Dopplersignal analysis and micro-Doppler feature extraction for classification of moving vehicles.The main work can be summarized as follows:
1. The principle of micro-Doppler effect and micro motion of rotation are introduced fora single scattering point and a target. Accordingly, the signal models of wheeled and trackedvehicles are established. Based on the signal model, measured data are analyzed todemonstrate distinctions between vehicles. The analysis results show the possibility of vehicleclassification using micro-Doppler signatures.
2. Some effective pre-preprocessing techniques for micro-Doppler signals of movingvehicles are studied. Firstly, clutter suppression is analyzed to show the purpose of clutterpre-preprocessing, which is simultaneously clutter suppression and signal preservation forclassification task. A band-limited CLEAN algorithm is introduced to realize the purpose, thismethod has low computation burden and requires only one frame of signal. Another techniqueis based on the assumption that clutter around target is stable. In this case, generalizedmatched filter (GMF) can be employed to suppress clutter. It is adaptive to the clutter due tothe utilization of priori information. Then the normalization of main bulk velocity is discussed.Since the change of bulk velocity usually affects the location of corresponding component andthe width of target spectrum in Doppler domain, which is undesired for classification, a velocity normalization method is proposed to eliminate the influence of the change of bulkvelocity. Finally, a nonlinear transform is introduced to enhance the function ofmicro-Doppler component in classification.
3. After analyzing the distinction between wheeled and tracked vehicles, a Dopplerspectrum based classification method is proposed. In this method,5features which isdescribed the figure of Doppler spectrum is extracted, and further classification process isimplemented. The classification results verify the effectiveness of the proposed method.
4. Information about energy distribution is used to discriminate wheeled and trackedvehicles. By the analysis of micro-Doppler signals, the returned signal of vehicles can beconsidered as the summation of harmonic components. Accordingly, the harmonic analysisbased method and eigenvalue spectrum based method are proposed. Although use differentapproaches to process micro-Doppler signals, these two methods essentially utilize the similarenergy distribution information to distinguish wheeled and tracked vehicles. The experimentresults show that energy distribution features well depict the distinction between wheeled andtracked vehicles.
5. Information associated with target structure is used to discriminate different types ofvehicles. In the returned signals of moving vehicles, the relation between differentmicro-Doppler frequency components reflects the structure information of micro motion parts.A hierarchical classification method based on empirical mode decomposition (EMD) ofmicro-Doppler signatures is proposed for moving vehicles, in which EMD is utilized fordecomposing the more detailed motion components of moving vehicles. Since the velocity ofthe upper track is always twice as large as the bulk velocity, which is determined by thespecial structure of track, this unique feature of tracked vehicle is utilized in the first stage ofour hierarchical structure to elementarily identify the tracked vehicle data. Then, if thefrequency induced by the upper track does not exist from some observation aspect-sectors, themicro-Doppler components are further characterized as the discriminative feature for the twokinds of vehicles in the second classification stage. In addition, clutter suppression can beachieved without extra pre-processing due to the adaptive decomposition characteristic ofEMD. Experiment results verify the effectiveness of the method and show the introduction ofstructure information is beneficial to improve classification performance.
6. The robust classification of moving vehicles is discussed. For moving vehicleclassification within short dwell time, how to handle ground clutter and receiver white noiseto obtain robust classification performance, and how to extract discriminative informationfrom micro-Doppler signatures as much as possible should be considered. According to theprinciple of compressive sensing (CS), classification method based on sparse representation is proposed. The method can not only adaptively remove ground clutter while preserve originalsignal as much as possible but also improve the classification performance especially underlow SNR conditions.
本文主要围绕国防预研等相关项目，紧密结合窄带短驻留时间条件下地面运动目标分类的工程应用背景，从信号预处理、微多普勒信号分析以及目标特征提取、车辆目标分类等方面展开相关理论和技术的研究。论文的主要工作概括如下：
1.对于旋转微运动形式的单散射点以及简单形式目标，介绍了其运动规律和微多普勒信号的数学表达式。在此基础上，针对车辆目标的特点进一步给出了车轮和履带的微多普勒回波信号的数学表达式。并利用车辆回波信号模型分析了实测数据中轮式车辆和履带式车辆的多普勒谱的差异性。分析结果表明，利用车辆目标回波中包含的微多普勒信息可以实现轮式车辆和履带式车辆的分类。
2.针对车辆目标分类的预处理问题，对一些通用的信号预处理方法进行了研究。在杂波抑制方面，指出分类问题中的杂波抑制处理要求对杂波进行消除的同时还需保留信号中微多普勒分量的结构。由此介绍了基于带限CLEAN算法的杂波抑制方法，该方法具有计算量较小，利用单帧回波即能实现的优点。同时，在目标周围杂波性质稳定的假设下，将杂波视为色噪声。由此介绍了基于广义匹配滤波器的杂波抑制方法，该方法对于杂波成分具有自适应性。这两种方法均能在有效抑制杂波成分的同时保留信号多普勒谱的结构，有利于分类信息的保留。在车身速度归一化方面，指出由车身速度变化带来的目标多普勒谱主峰平移和多普勒谱展宽会对分类性能产生影响，由此提出了目标回波信号的车身速度归一化方法，用以消除车身速度的变化对分类结果产生的影响。在微多普勒信息提取方面，分析了车辆回波信号特点的基础上，介绍了目标多普勒谱的非线性变换预处理，用以增强微多普勒成分在分类中的作用。
3.分析了轮式车辆和履带式车辆在多普勒谱形状上的差异性，提出基于目标多普勒谱的车辆分类方法。提取了波形熵，主副峰值比等5种描述多普勒谱形状的特征，并利用这些特征对车辆进行了分类。实验结果表明了方法的有效性。
4.研究了基于能量分布特征的车辆目标分类方法。在分析轮式和履带式车辆回波结构的基础上，指出车辆目标的回波可视为谐波和的形式。提出了基于CLEAN算法谐波分析和信号特征谱的车辆分类方法。这两种方法本质上均利用了轮式和履带式车辆回波中各次谐波成分在能量分布上的差异性。实验结果表明，能量分布特征能够有效区分轮式车辆和履带式车辆。
5.研究了车辆目标分类中，一些能够反映目标结构信息的重要微多普勒分量的利用问题。分析了目标回波中多普勒频率分量之间的相互关系，指出多普勒频率分量之间的相对位置信息反映了目标的结构特点。提出了一种基于经验模态分解（EMD）的车辆目标分层分类结构，该结构在第一层中根据2v分量的存在性初步判别履带式车辆，第二层中则对不存在2v分量的信号进行进一步的分类。该方法提取了目标多普勒谱中反映出的车辆微运动部件的结构信息，并利用这些信息对车辆目标进行分类。此外，由于算法的自适应性，杂波抑制过程也自然的包含于处理过程中。实验结果验证了方法的有效性，说明多普勒分量之间相对位置信息的引入有利于提高分类性能。
6.研究了车辆目标分类方法中的稳健分类问题。指出在窄带短驻留时间条件下，车辆目标分类面临杂波、噪声和低分辨率三个问题。利用压缩感知（CS）的思想，提出了基于回波信号稀疏重构的车辆分类方法，该方法能在抑制杂波和噪声的同时，提高目标多普勒谱的分辨率。在提取微多普勒信息更加准确的基础上，使得分类性能对于信噪比变化具有稳健性。
Micro-Doppler effect was firstly introduced in coherent laser radar system. It containsinformation about the characteristics of interested moving target, and can be seen as a uniquesignature of the target. The research on micro-Doppler provides a new prospect for radarautomatic target recognition (ATR) and it is also a complementary to existing methods.Wheeled and tracked vehicles often undertake different tasks in battle, which determines theyhave different threat degrees, thus the discrimination of the two types of vehicles is importantto a successful tactic. Since vehicles have typical micro motion structures which will inducemicro-doppler modulation on radar signal, micro-Doppler information contained in theirreturned signals can be utilized to discriminant different moving vehicles. Furthermore, fornarrow band radar system, the research of micro-Doppler effect is beneficial to improve theclassification function. In addition, the research in short dwell time condition could help tosolve the contradiction of time resource distribution between various modern radar functions.
This dissertation is supported by Advanced Defense Research Programs of China.Considering the engineering background of the narrow band radar target classification withinshort dwell time, the dissertation studies micro-Doppler signal pre-processing, micro-Dopplersignal analysis and micro-Doppler feature extraction for classification of moving vehicles.The main work can be summarized as follows:
1. The principle of micro-Doppler effect and micro motion of rotation are introduced fora single scattering point and a target. Accordingly, the signal models of wheeled and trackedvehicles are established. Based on the signal model, measured data are analyzed todemonstrate distinctions between vehicles. The analysis results show the possibility of vehicleclassification using micro-Doppler signatures.
2. Some effective pre-preprocessing techniques for micro-Doppler signals of movingvehicles are studied. Firstly, clutter suppression is analyzed to show the purpose of clutterpre-preprocessing, which is simultaneously clutter suppression and signal preservation forclassification task. A band-limited CLEAN algorithm is introduced to realize the purpose, thismethod has low computation burden and requires only one frame of signal. Another techniqueis based on the assumption that clutter around target is stable. In this case, generalizedmatched filter (GMF) can be employed to suppress clutter. It is adaptive to the clutter due tothe utilization of priori information. Then the normalization of main bulk velocity is discussed.Since the change of bulk velocity usually affects the location of corresponding component andthe width of target spectrum in Doppler domain, which is undesired for classification, a velocity normalization method is proposed to eliminate the influence of the change of bulkvelocity. Finally, a nonlinear transform is introduced to enhance the function ofmicro-Doppler component in classification.
3. After analyzing the distinction between wheeled and tracked vehicles, a Dopplerspectrum based classification method is proposed. In this method,5features which isdescribed the figure of Doppler spectrum is extracted, and further classification process isimplemented. The classification results verify the effectiveness of the proposed method.
4. Information about energy distribution is used to discriminate wheeled and trackedvehicles. By the analysis of micro-Doppler signals, the returned signal of vehicles can beconsidered as the summation of harmonic components. Accordingly, the harmonic analysisbased method and eigenvalue spectrum based method are proposed. Although use differentapproaches to process micro-Doppler signals, these two methods essentially utilize the similarenergy distribution information to distinguish wheeled and tracked vehicles. The experimentresults show that energy distribution features well depict the distinction between wheeled andtracked vehicles.
5. Information associated with target structure is used to discriminate different types ofvehicles. In the returned signals of moving vehicles, the relation between differentmicro-Doppler frequency components reflects the structure information of micro motion parts.A hierarchical classification method based on empirical mode decomposition (EMD) ofmicro-Doppler signatures is proposed for moving vehicles, in which EMD is utilized fordecomposing the more detailed motion components of moving vehicles. Since the velocity ofthe upper track is always twice as large as the bulk velocity, which is determined by thespecial structure of track, this unique feature of tracked vehicle is utilized in the first stage ofour hierarchical structure to elementarily identify the tracked vehicle data. Then, if thefrequency induced by the upper track does not exist from some observation aspect-sectors, themicro-Doppler components are further characterized as the discriminative feature for the twokinds of vehicles in the second classification stage. In addition, clutter suppression can beachieved without extra pre-processing due to the adaptive decomposition characteristic ofEMD. Experiment results verify the effectiveness of the method and show the introduction ofstructure information is beneficial to improve classification performance.
6. The robust classification of moving vehicles is discussed. For moving vehicleclassification within short dwell time, how to handle ground clutter and receiver white noiseto obtain robust classification performance, and how to extract discriminative informationfrom micro-Doppler signatures as much as possible should be considered. According to theprinciple of compressive sensing (CS), classification method based on sparse representation is proposed. The method can not only adaptively remove ground clutter while preserve originalsignal as much as possible but also improve the classification performance especially underlow SNR conditions.
引文
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