雷达目标高分辨距离像仿真与识别技术研究
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摘要
雷达自动目标识别技术在军事和民用上拥有巨大的应用价值。随着宽带雷达技术的逐渐成熟，雷达可以获取更多的目标信息，这为雷达自动目标识别技术的发展提供了强有力的支持。作为一种宽带雷达目标回波形式，高分辨距离像（HRRP）是雷达目标回波沿距离维的分布，它可以反映出目标的结构信息，并且具有获取时间短，处理简单等优点，在雷达自动目标识别领域具有较大应用价值。本文主要工作围绕HRRP识别展开，分为两部分：第一部分对雷达目标高频电磁散射计算和HRRP仿真开展研究，开发一套具有工程应用价值的雷达目标高频电磁散射特性分析系统，为雷达目标HRRP识别技术的研发提供具有较高置信度的数据支持。第二部分在仿真数据的基础上对雷达目标HRRP识别技术开展研究，主要针对雷达目标HRRP识别中的目标姿态敏感性问题研究基于特征加权系数优化的HRRP识别、基于半参数化统计模型的HRRP识别和基于子空间特征提取的HRRP识别，目的是为今后的实际工程应用做出有益的探索和技术积累。
本文主要研究工作与贡献如下：
1、雷达目标HRRP识别技术的研发离不开HRRP数据的支持，本文主要采用电磁仿真手段建立HRRP数据库。根据雷达目标电磁散射计算的特点和目标识别应用方向，研究了基于高频渐近技术的雷达目标高频电磁散射计算和HRRP仿真。此外，针对具有超电大尺寸的舰船类目标，研究了近场目标高频电磁散射计算和HRRP仿真，并采用基于KdTree的射线跟踪加速算法和基于高性能计算（HPC）的任务级并行计算对高频电磁散射计算做了加速改进。最后，在理论工作的基础上，研发了一套集几何建模、高频电磁散射计算以及数据分析为一体的极具航空航天特色的雷达目标高频电磁散射特性分析系统。
2、研究了基于特征加权系数优化的HRRP识别。由于特征加权距离像模板可以看作是一种与HRRP回波统计模型无关的距离像模板，它利用特征加权系数对HRRP输入特征空间进行坐标尺度变换，以此改变原始输入特征空间中的几何距离度量关系，从而避免了对HRRP距离分辨单元回波幅值统计模型的建立。本文通过定义HRRP样本在特征尺度变换空间中的可分性度量，设计了一种HRRP特征加权系数优化目标函数，并在此基础上提出了一种基于特征加权系数优化的HRRP识别算法。同时，本文还研究了基于核方法的特征加权系数优化，给出了一种针对支持向量机（SVM）分类器的HRRP特征加权系数优化方法，该方法可以做到将核参数优化过程和SVM求解过程统一起来。
3、研究了基于半参数化统计模型的HRRP识别。针对因HRRP各距离分辨单元回波幅值统计分布的复杂性而面临的类条件概率密度模型选择问题，提出一种基于半参数化概率密度估计的雷达目标识别方法，该方法将HRRP各距离分辨单元回波幅值统计分布模型统一起来，达到了概率密度估计中参数化方法和非参数化方法优缺互补的目的。同时，为了解决基于Parzen窗函数的半参数化概率密度估计在数据量较大时执行效率下降的问题，提出一种基于核方法的半参数化概率密度估计方法，该方法有效降低了概率密度函数表出所需样本量，提高了计算效率。
4、研究了基于子空间特征提取的HRRP识别。本文主要研究了核Fisher判别分析（KFDA）和核主分量分析（KPCA）两种子空间特征提取技术在雷达目标HRRP识别中的应用。对于KFDA，针对Fisher判别准则在核参数优化过程中可能面临的数值不稳定问题，本文采用Fisher判别准则下界作为核参数优化目标函数对KFDA进行优化，并将其应用于HRRP特征提取与目标识别。对于KPCA，本文提出一种基于KPCA重构的雷达目标HRRP识别方法，该方法通过计算HRRP样本在高维特征空间中的重构误差来代替距离度量，解决了HRRP幅值分布的非高斯性问题，并松弛了角域划分要求。
Radar automatic target recognition (RATR) plays an important role in military and civilian field.With the development of wideband radar techniques, more and more useful target information can beobtained, and it provides strong supports for RATR. As a form of wideband radar target returned ech-oes, high-resolution range profile (HRRP) is the amplitude distribution of returned echoes along theradar line-of-sight, and it contains target structure information. Moreover, HRRP has the advantagesof easy acquisition and processing, thereby radar target HRRP recognition is of great value in theRATR community. This thesis focuses on radar target HRRP recognition, and mainly consists of twoparts. In the first part, high-frequency electromagnetic scattering calculation and HRRP simulation forcomplex radar targets are studied and corresponding valuable software is also developed for engi-neering, which aims at providing relatively high quality data support for radar target HRRP recogni-tion. In the second part, based on the simulated HRRP dataset, radar target HRRP recognition is re-searched from tree aspects for solving HRRP target aspect sensitivity problem, including feature scal-ing coefficients optimization, semi-parametric statistical modeling, and subspace feature extraction.Altogether, the main purpose of this thesis intends to make a useful exploration and technology ac-cumulation for RATR engineering application in the future.
In summary, the main contents and contribution of this thesis are listed as follows:
1. Research and development of radar target HRRP recognition can not do without the support ofHRRP data. In this thesis, HRRP dataset is established by electromagnetic scattering simulation. Ac-cording to the feature of electromagnetic scattering calculation for radar targets and for the purpose ofRATR application, high-frequency and asymptotic method for radar target high-frequency electro-magnetic scattering calculation and HRRP simulation is adopted. In addition, near field high-fre-quency electromagnetic scattering and HRRP simulation for extremely electrically large radar target,like ship target, is also studied. Meanwhile, KdTree based ray tracing algorithm and high performancecomputing (HPC) based task-level parallelism are both used for calculating acceleration. Finally, onthe basis of theoretical work, the radar target high-frequency electromagnetic scattering characteristicsanalysis software containing geometric modeling, high-frequency electromagnetic scattering calcula-tion, and data analyzing is developed.
2. Feature scaling coefficients optimization based HRRP recognition is studied. For the reasonthat feature scaling HRRP template can be seem as a template which is independent with statistic model of HRRP returned echoes, it changes the geometric distance metric compared with originalHRRP feature space by coordinate-scale transformation, and avoids statistic model establishing forHRRP returned echoes. In this thesis, by defining the separability criterion of HRRP patterns in thetransformed feature scaling space, a HRRP feature scaling coefficients optimization objective functionis designed, and a HRRP recognition approach based on feature scaling optimization is proposed. Be-sides, feature scaling coefficients optimization based on kernel method is also studied. This thesisprovides a HRRP feature scaling coefficients optimization approach in view of the support vectormachine (SVM) classifier. The given approach works well owing to the consistency between kerneloptimization and SVM solution.
3. Semi-parametric statistical modeling based HRRP recognition is studied. According to theclass-conditional probability density model selection problem caused by the complex statistic distri-bution of returned echo in each HRRP range cells, a radar HRRP recognition approach based on thesemi-parametric probability density is proposed. The proposed approach unifies the statistic distribu-tion model, and both advantages of parametric method and nonparametric method are merged in thesemi-parametric density estimation. However, when the Parzen window based semi-parametric isused and large data quantity is appeared, the execution efficiency reduces. In order to solve this prob-lem, a semi-parametric probability density approach based on kernel method is proposed. The pro-posed approach reduces the needs of samples for probability density function representation, and im-proves the computational efficiency.
4. Subspace feature extraction based HRRP recognition is studied. In this thesis, two subspacefeature extraction methods including kernel fisher discriminant analysis (KFDA) and kernel principalcomponent analysis (KPCA) is applied to HRRP recognition. For KFDA, considering the numericalinstability problem when optimizing kernel parameters using Fisher’s discriminant criterion, the lowerbound of Fisher’s discriminant criterion is used as the kernel parameter optimization objective func-tion for KFDA, and applied it to HRRP feature extraction and recognition. For KPCA, an approachbased on kernel principle component analysis reconstruction is proposed. To this approach, the type oftest sample is determined by the minimum reconstruction error instead of distance measure, whichsolves the HRRP non-Guassian distribution problems and relaxes the angle division rules.
本文主要研究工作与贡献如下：
1、雷达目标HRRP识别技术的研发离不开HRRP数据的支持，本文主要采用电磁仿真手段建立HRRP数据库。根据雷达目标电磁散射计算的特点和目标识别应用方向，研究了基于高频渐近技术的雷达目标高频电磁散射计算和HRRP仿真。此外，针对具有超电大尺寸的舰船类目标，研究了近场目标高频电磁散射计算和HRRP仿真，并采用基于KdTree的射线跟踪加速算法和基于高性能计算（HPC）的任务级并行计算对高频电磁散射计算做了加速改进。最后，在理论工作的基础上，研发了一套集几何建模、高频电磁散射计算以及数据分析为一体的极具航空航天特色的雷达目标高频电磁散射特性分析系统。
2、研究了基于特征加权系数优化的HRRP识别。由于特征加权距离像模板可以看作是一种与HRRP回波统计模型无关的距离像模板，它利用特征加权系数对HRRP输入特征空间进行坐标尺度变换，以此改变原始输入特征空间中的几何距离度量关系，从而避免了对HRRP距离分辨单元回波幅值统计模型的建立。本文通过定义HRRP样本在特征尺度变换空间中的可分性度量，设计了一种HRRP特征加权系数优化目标函数，并在此基础上提出了一种基于特征加权系数优化的HRRP识别算法。同时，本文还研究了基于核方法的特征加权系数优化，给出了一种针对支持向量机（SVM）分类器的HRRP特征加权系数优化方法，该方法可以做到将核参数优化过程和SVM求解过程统一起来。
3、研究了基于半参数化统计模型的HRRP识别。针对因HRRP各距离分辨单元回波幅值统计分布的复杂性而面临的类条件概率密度模型选择问题，提出一种基于半参数化概率密度估计的雷达目标识别方法，该方法将HRRP各距离分辨单元回波幅值统计分布模型统一起来，达到了概率密度估计中参数化方法和非参数化方法优缺互补的目的。同时，为了解决基于Parzen窗函数的半参数化概率密度估计在数据量较大时执行效率下降的问题，提出一种基于核方法的半参数化概率密度估计方法，该方法有效降低了概率密度函数表出所需样本量，提高了计算效率。
4、研究了基于子空间特征提取的HRRP识别。本文主要研究了核Fisher判别分析（KFDA）和核主分量分析（KPCA）两种子空间特征提取技术在雷达目标HRRP识别中的应用。对于KFDA，针对Fisher判别准则在核参数优化过程中可能面临的数值不稳定问题，本文采用Fisher判别准则下界作为核参数优化目标函数对KFDA进行优化，并将其应用于HRRP特征提取与目标识别。对于KPCA，本文提出一种基于KPCA重构的雷达目标HRRP识别方法，该方法通过计算HRRP样本在高维特征空间中的重构误差来代替距离度量，解决了HRRP幅值分布的非高斯性问题，并松弛了角域划分要求。
Radar automatic target recognition (RATR) plays an important role in military and civilian field.With the development of wideband radar techniques, more and more useful target information can beobtained, and it provides strong supports for RATR. As a form of wideband radar target returned ech-oes, high-resolution range profile (HRRP) is the amplitude distribution of returned echoes along theradar line-of-sight, and it contains target structure information. Moreover, HRRP has the advantagesof easy acquisition and processing, thereby radar target HRRP recognition is of great value in theRATR community. This thesis focuses on radar target HRRP recognition, and mainly consists of twoparts. In the first part, high-frequency electromagnetic scattering calculation and HRRP simulation forcomplex radar targets are studied and corresponding valuable software is also developed for engi-neering, which aims at providing relatively high quality data support for radar target HRRP recogni-tion. In the second part, based on the simulated HRRP dataset, radar target HRRP recognition is re-searched from tree aspects for solving HRRP target aspect sensitivity problem, including feature scal-ing coefficients optimization, semi-parametric statistical modeling, and subspace feature extraction.Altogether, the main purpose of this thesis intends to make a useful exploration and technology ac-cumulation for RATR engineering application in the future.
In summary, the main contents and contribution of this thesis are listed as follows:
1. Research and development of radar target HRRP recognition can not do without the support ofHRRP data. In this thesis, HRRP dataset is established by electromagnetic scattering simulation. Ac-cording to the feature of electromagnetic scattering calculation for radar targets and for the purpose ofRATR application, high-frequency and asymptotic method for radar target high-frequency electro-magnetic scattering calculation and HRRP simulation is adopted. In addition, near field high-fre-quency electromagnetic scattering and HRRP simulation for extremely electrically large radar target,like ship target, is also studied. Meanwhile, KdTree based ray tracing algorithm and high performancecomputing (HPC) based task-level parallelism are both used for calculating acceleration. Finally, onthe basis of theoretical work, the radar target high-frequency electromagnetic scattering characteristicsanalysis software containing geometric modeling, high-frequency electromagnetic scattering calcula-tion, and data analyzing is developed.
2. Feature scaling coefficients optimization based HRRP recognition is studied. For the reasonthat feature scaling HRRP template can be seem as a template which is independent with statistic model of HRRP returned echoes, it changes the geometric distance metric compared with originalHRRP feature space by coordinate-scale transformation, and avoids statistic model establishing forHRRP returned echoes. In this thesis, by defining the separability criterion of HRRP patterns in thetransformed feature scaling space, a HRRP feature scaling coefficients optimization objective functionis designed, and a HRRP recognition approach based on feature scaling optimization is proposed. Be-sides, feature scaling coefficients optimization based on kernel method is also studied. This thesisprovides a HRRP feature scaling coefficients optimization approach in view of the support vectormachine (SVM) classifier. The given approach works well owing to the consistency between kerneloptimization and SVM solution.
3. Semi-parametric statistical modeling based HRRP recognition is studied. According to theclass-conditional probability density model selection problem caused by the complex statistic distri-bution of returned echo in each HRRP range cells, a radar HRRP recognition approach based on thesemi-parametric probability density is proposed. The proposed approach unifies the statistic distribu-tion model, and both advantages of parametric method and nonparametric method are merged in thesemi-parametric density estimation. However, when the Parzen window based semi-parametric isused and large data quantity is appeared, the execution efficiency reduces. In order to solve this prob-lem, a semi-parametric probability density approach based on kernel method is proposed. The pro-posed approach reduces the needs of samples for probability density function representation, and im-proves the computational efficiency.
4. Subspace feature extraction based HRRP recognition is studied. In this thesis, two subspacefeature extraction methods including kernel fisher discriminant analysis (KFDA) and kernel principalcomponent analysis (KPCA) is applied to HRRP recognition. For KFDA, considering the numericalinstability problem when optimizing kernel parameters using Fisher’s discriminant criterion, the lowerbound of Fisher’s discriminant criterion is used as the kernel parameter optimization objective func-tion for KFDA, and applied it to HRRP feature extraction and recognition. For KPCA, an approachbased on kernel principle component analysis reconstruction is proposed. To this approach, the type oftest sample is determined by the minimum reconstruction error instead of distance measure, whichsolves the HRRP non-Guassian distribution problems and relaxes the angle division rules.
引文
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