基于统计建模的雷达高分辨距离像目标识别方法研究
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摘要
随着现代战争信息化和智能化水平的提高，军方对雷达自动目标识别的需求愈加迫切。与窄带雷达相比，宽带雷达有着更高的距离分辨率，能够提供更多的目标结构信息，因此基于宽带雷达信号的目标识别受到广泛关注。常用的宽带信号有高分辨距离像(HRRP)和合成孔径雷达像(包括SAR和ISAR像)两类，与后者相比，HRRP具有易于获取和处理简单等优势，逐渐成为目标识别领域的研究热点。
本论文主要围绕国防预研等相关项目，结合雷达HRRP目标识别的理论与工程应用背景，从特征提取、统计建模以及噪声稳健识别等方面展开相关研究。论文的主要工作概括如下：
1.分析了HRRP的方位敏感性，阐明了对HRRP样本分帧建模的原因，并提出了一种基于子空间模型和Kullback-Leibler距离的子帧合并分帧方法。实验结果表明，该方法能够按照目标姿态的变化将相似分布的样本划分到同一帧中。因此，该方法不但降低了各类目标的总帧数而且提高了系统的识别性能。
2.研究了噪声背景下的识别问题。许多工作都假设训练和测试样本具有很高的信噪比，在识别中忽略了噪声的影响。但在实际战场条件下，目标回波中总是不可避免地含有噪声，而训练与测试样本间信噪比的失配会使识别性能恶化。我们回顾了已有的噪声稳健识别方法，指出了它们的优势与不足。然后提出了一种新的噪声稳健识别思路，即向训练样本中加入噪声来学习各种信噪比下的模板，并在测试阶段选择相应信噪比下的模板进行匹配。实验结果表明这种方法能够避免训练与测试样本间信噪比的失配，改善了系统在低信噪比条件下的识别性能。
3.研究了HRRP识别中的特征提取问题。原始的HRRP数据具有较高的维度和较多的敏感性，给识别工作带来了不便。我们分析了HRRP频谱幅度的统计特性，提出使用自回归模型对频谱幅度的广义平稳性建模，并提取自回归系数和偏相关系数作为识别特征。这两种特征维度较低，同时具有平移和强度不变性，而且保留了频谱幅度的结构信息。此外，为了克服HRRP的方位敏感性，我们提出了一种基于混合高斯模型的自适应分帧算法。该算法不但可以自动确定总帧数，而且能够保证各帧内样本统计特性一致，避免了样本与模型失配。
4.研究了小样本识别问题。HRRP具有较高维度，而实际获取的样本数量又很有限，因此HRRP识别是一个典型的小样本学习问题。我们提出使用HRRP频谱幅度(以下简称频幅)作为识别特征，并沿频率维对其序列建模。这种建模方式将频幅视为一维的序列数据，从而既达到了降维的目的，又避免了传统降维方法中的信息损失。我们首先假设频幅分量服从高斯分布，采用线性动态模型对频幅序列建模。该模型能够描述频幅序列的广义平稳性，具有较好的识别性能。之后我们进一步分析了频幅分量的统计特性，认为其服从多模分布，于是采用混合自回归模型对频幅序列建模。由于该模型更加准确地描述了频幅的统计特性，因此其识别性能进一步提高。以上序列模型自由度都很低，而且使用一个样本就能估计它们所有的参数，这些性质使得它们能够在小样本条件下具有良好性能。
5.研究了统计建模与模型选择的问题。HRRP服从联合非高斯分布，且样本间有较强的时序相关性。我们提出使用局部因子分析模型对HRRP的非高斯性和维间相关性建模，使用时序因子分析模型对HRRP的子空间结构和时序相关性建模。这二者对HRRP统计特性描述地更加准确，所以识别性能较传统模型有明显特高。另外传统的模型选择方法存在着计算量大、模型选择准则评估不可靠的问题。为此，我们采用Bayesian Ying-Yang (BYY)和谐学习理论进行模型学习，它能够在估计参数的同时自动完成模型选择。与传统方法相比，BYY学习显著降低了计算量，并提高了参数估计和模型选择的精度。
6.研究了复HRRP识别问题。我们分析了初相对复HRRP统计特性的影响，指出初相变化并不影响复HRRP的统计分布，进而提出采用复数因子分析(CFA)模型对复HRRP统计建模。由于利用了复HRRP中的相位信息，CFA模型的识别性能要优于FA模型。另外，我们同时提出一种针对CFA模型的噪声自适应修正算法。在复HRRP识别中，含噪测试样本内的噪声完全为加性噪声，修正模型参数时将不存在实HRRP识别中的模型-数据失配问题，因此该算法会有更好的噪声稳健性。
With the application of information and intelligence technologies in modernwarfare, there is an urgent need for Radar Automatic Target Recognition (RATR).Wideband radar can provide abundant target structure information, and thus hasreceived intensive attention from the RATR community. The general wideband signalsinclude High Resolution Range Profile (HRRP) and Synthesized Aperture Radar (SAR)image or Inverse SAR image. Since the former is easily obtained and computationallyefficient, HRRP based recognition is more attractive in many circumstances. Thisdissertation focuses on HRRP recognition from feature extraction, statistical modelingand noise robustness, etc. The main research efforts are summarized as follows.
1. The first part analyzes the target-aspect sensitivity of HRRP and proposes a newframe partition method which is based on subspace modeling and Kullback-Leiblerdistance. Experimental results show that the new method can allocate those samplesfollowing the similar statistical distribution into a same frame, and hence decrease thetotal frame number of all targets and improve the final recognition performance.
2. The second part focuses on noise robust recognition. In many literatures, it wasassumed that the training and testing samples were measured under high signal-to-noise(SNR) ratio conditions, and the effect of noise was ignored. However, in practicalapplications the testing samples are unavoidably contaminated by noise, andconsequently the SNR mismatches between training and testing samples will deterioratethe recognition performance. We firstly make a review of existing methods and thenpropose a new framework for noise robust recognition. In the training phase, we learndifferent models for different SNRs by adding noise into the clean samples, while in thetesting phase we choose appropriate models for recognition based on the estimated SNRvalue of testing sample. Experimental results show the new framework can considerablyimprove the recognition performance under low SNR conditions.
3. In the third part, feature extraction from HRRP is discussed. HRRP ischaracterized by high-dimensionality and four sensitivities, which pose hurdles forHRRP based target recognition. After analyzing the statistical property of HRRP’sfrequency spectrum amplitude (FSA), we propose to model FSA by Autoregressive (AR)model and extract the AR coefficients and partial correlation coefficients as recognitionfeatures. Both the two features are low-dimensional and invariant to translation and amplitude-scale changes of HRRP, and more importantly, they preserve most of thestructure information of FSA. In addition, to tackle the remaining target-aspectsensitivity, we propose a Gaussian mixture model based frame partition method whichcan determine the frame number automatically and guarantee the distributionconsistency of samples in each frame.
4. The fourth part manages to fulfill the recognition task under small trainingsample size condition. Both the high feature dimensionality and limited sample sizemake HRRP based recognition a typical small sample learning problem. To cope withthis, we adopt FSA as recognition feature and model it along the frequency dimensionby sequential models. In this way, we relax the heavy requirement of training samples inHRRP recognition. Firstly, assuming the FSA components are Gaussian distributed, weemploy Linear Dynamic model to describe FSA. This model can well capture thestationarity of FSA and thus obtain satisfactory recognition performance. Afterwards,experimental results based on measured data reveal that the Gaussian assumption isinappropriate, hence we further make a study on more accurate statistical modeling.Mixture Autoregressive model is thus introduced to describe the stationarity andmulti-modality of FSA simultaneously which show incremental improvements onrecognition accuracy. The models mentioned above have low degrees of freedom and alltheir parameters can be estimated via a single sample, which collectively make theirrecognition performance robust to the variation of training sample size.
5. In the fifth part, we investigate accurate statistical modeling and model selectionproblem. HRRP samples are temporally dependent and jointly non-Gaussian distributed,the statistical modeling of which is a challenging task for HRRP based recognition. Ourmain work includes:1) adopting Local Factor Analysis model to describe thenon-Gaussian property and inter-dimensional dependency of HRRP;2) employingTemporal Factor Analysis model to describe the spatio-temporal structure of HRRP.Since both the two models can describe HRRP more accurately, they achieve betterperformance than traditional models. Furthermore, the conventional two-phaseapproach for model learning suffers from intensive computation burden and unreliableevaluation. To tackle these problems, we adopt Bayesian Ying-Yang (BYY) harmonylearning that has automatic model selection ability during parameter estimation.Experimental results illustrate that the BYY learning can significantly decrease thecomputation burden while improving the accuracy of parameter estimation and modelselection.
6. The last part demonstrates the feasibility of recognition using complex HRRP. Based on the theoretical analysis, it is pointed out that the variation of initial-phase hasno effect on the statistical distribution of HRRP. So it is natural to generalize the FAmodel to the complex domain and model complex HRRP directly. By using additionalphase information in complex HRRP, the complex FA (CFA) model is superior to the FAmodel in recognition performance. Also, we develop a noise adaptive modificationalgorithm for the CFA model. Since there is no approximation made in the modificationstep, this algorithm can gain better performance under low SNR conditions.
本论文主要围绕国防预研等相关项目，结合雷达HRRP目标识别的理论与工程应用背景，从特征提取、统计建模以及噪声稳健识别等方面展开相关研究。论文的主要工作概括如下：
1.分析了HRRP的方位敏感性，阐明了对HRRP样本分帧建模的原因，并提出了一种基于子空间模型和Kullback-Leibler距离的子帧合并分帧方法。实验结果表明，该方法能够按照目标姿态的变化将相似分布的样本划分到同一帧中。因此，该方法不但降低了各类目标的总帧数而且提高了系统的识别性能。
2.研究了噪声背景下的识别问题。许多工作都假设训练和测试样本具有很高的信噪比，在识别中忽略了噪声的影响。但在实际战场条件下，目标回波中总是不可避免地含有噪声，而训练与测试样本间信噪比的失配会使识别性能恶化。我们回顾了已有的噪声稳健识别方法，指出了它们的优势与不足。然后提出了一种新的噪声稳健识别思路，即向训练样本中加入噪声来学习各种信噪比下的模板，并在测试阶段选择相应信噪比下的模板进行匹配。实验结果表明这种方法能够避免训练与测试样本间信噪比的失配，改善了系统在低信噪比条件下的识别性能。
3.研究了HRRP识别中的特征提取问题。原始的HRRP数据具有较高的维度和较多的敏感性，给识别工作带来了不便。我们分析了HRRP频谱幅度的统计特性，提出使用自回归模型对频谱幅度的广义平稳性建模，并提取自回归系数和偏相关系数作为识别特征。这两种特征维度较低，同时具有平移和强度不变性，而且保留了频谱幅度的结构信息。此外，为了克服HRRP的方位敏感性，我们提出了一种基于混合高斯模型的自适应分帧算法。该算法不但可以自动确定总帧数，而且能够保证各帧内样本统计特性一致，避免了样本与模型失配。
4.研究了小样本识别问题。HRRP具有较高维度，而实际获取的样本数量又很有限，因此HRRP识别是一个典型的小样本学习问题。我们提出使用HRRP频谱幅度(以下简称频幅)作为识别特征，并沿频率维对其序列建模。这种建模方式将频幅视为一维的序列数据，从而既达到了降维的目的，又避免了传统降维方法中的信息损失。我们首先假设频幅分量服从高斯分布，采用线性动态模型对频幅序列建模。该模型能够描述频幅序列的广义平稳性，具有较好的识别性能。之后我们进一步分析了频幅分量的统计特性，认为其服从多模分布，于是采用混合自回归模型对频幅序列建模。由于该模型更加准确地描述了频幅的统计特性，因此其识别性能进一步提高。以上序列模型自由度都很低，而且使用一个样本就能估计它们所有的参数，这些性质使得它们能够在小样本条件下具有良好性能。
5.研究了统计建模与模型选择的问题。HRRP服从联合非高斯分布，且样本间有较强的时序相关性。我们提出使用局部因子分析模型对HRRP的非高斯性和维间相关性建模，使用时序因子分析模型对HRRP的子空间结构和时序相关性建模。这二者对HRRP统计特性描述地更加准确，所以识别性能较传统模型有明显特高。另外传统的模型选择方法存在着计算量大、模型选择准则评估不可靠的问题。为此，我们采用Bayesian Ying-Yang (BYY)和谐学习理论进行模型学习，它能够在估计参数的同时自动完成模型选择。与传统方法相比，BYY学习显著降低了计算量，并提高了参数估计和模型选择的精度。
6.研究了复HRRP识别问题。我们分析了初相对复HRRP统计特性的影响，指出初相变化并不影响复HRRP的统计分布，进而提出采用复数因子分析(CFA)模型对复HRRP统计建模。由于利用了复HRRP中的相位信息，CFA模型的识别性能要优于FA模型。另外，我们同时提出一种针对CFA模型的噪声自适应修正算法。在复HRRP识别中，含噪测试样本内的噪声完全为加性噪声，修正模型参数时将不存在实HRRP识别中的模型-数据失配问题，因此该算法会有更好的噪声稳健性。
With the application of information and intelligence technologies in modernwarfare, there is an urgent need for Radar Automatic Target Recognition (RATR).Wideband radar can provide abundant target structure information, and thus hasreceived intensive attention from the RATR community. The general wideband signalsinclude High Resolution Range Profile (HRRP) and Synthesized Aperture Radar (SAR)image or Inverse SAR image. Since the former is easily obtained and computationallyefficient, HRRP based recognition is more attractive in many circumstances. Thisdissertation focuses on HRRP recognition from feature extraction, statistical modelingand noise robustness, etc. The main research efforts are summarized as follows.
1. The first part analyzes the target-aspect sensitivity of HRRP and proposes a newframe partition method which is based on subspace modeling and Kullback-Leiblerdistance. Experimental results show that the new method can allocate those samplesfollowing the similar statistical distribution into a same frame, and hence decrease thetotal frame number of all targets and improve the final recognition performance.
2. The second part focuses on noise robust recognition. In many literatures, it wasassumed that the training and testing samples were measured under high signal-to-noise(SNR) ratio conditions, and the effect of noise was ignored. However, in practicalapplications the testing samples are unavoidably contaminated by noise, andconsequently the SNR mismatches between training and testing samples will deterioratethe recognition performance. We firstly make a review of existing methods and thenpropose a new framework for noise robust recognition. In the training phase, we learndifferent models for different SNRs by adding noise into the clean samples, while in thetesting phase we choose appropriate models for recognition based on the estimated SNRvalue of testing sample. Experimental results show the new framework can considerablyimprove the recognition performance under low SNR conditions.
3. In the third part, feature extraction from HRRP is discussed. HRRP ischaracterized by high-dimensionality and four sensitivities, which pose hurdles forHRRP based target recognition. After analyzing the statistical property of HRRP’sfrequency spectrum amplitude (FSA), we propose to model FSA by Autoregressive (AR)model and extract the AR coefficients and partial correlation coefficients as recognitionfeatures. Both the two features are low-dimensional and invariant to translation and amplitude-scale changes of HRRP, and more importantly, they preserve most of thestructure information of FSA. In addition, to tackle the remaining target-aspectsensitivity, we propose a Gaussian mixture model based frame partition method whichcan determine the frame number automatically and guarantee the distributionconsistency of samples in each frame.
4. The fourth part manages to fulfill the recognition task under small trainingsample size condition. Both the high feature dimensionality and limited sample sizemake HRRP based recognition a typical small sample learning problem. To cope withthis, we adopt FSA as recognition feature and model it along the frequency dimensionby sequential models. In this way, we relax the heavy requirement of training samples inHRRP recognition. Firstly, assuming the FSA components are Gaussian distributed, weemploy Linear Dynamic model to describe FSA. This model can well capture thestationarity of FSA and thus obtain satisfactory recognition performance. Afterwards,experimental results based on measured data reveal that the Gaussian assumption isinappropriate, hence we further make a study on more accurate statistical modeling.Mixture Autoregressive model is thus introduced to describe the stationarity andmulti-modality of FSA simultaneously which show incremental improvements onrecognition accuracy. The models mentioned above have low degrees of freedom and alltheir parameters can be estimated via a single sample, which collectively make theirrecognition performance robust to the variation of training sample size.
5. In the fifth part, we investigate accurate statistical modeling and model selectionproblem. HRRP samples are temporally dependent and jointly non-Gaussian distributed,the statistical modeling of which is a challenging task for HRRP based recognition. Ourmain work includes:1) adopting Local Factor Analysis model to describe thenon-Gaussian property and inter-dimensional dependency of HRRP;2) employingTemporal Factor Analysis model to describe the spatio-temporal structure of HRRP.Since both the two models can describe HRRP more accurately, they achieve betterperformance than traditional models. Furthermore, the conventional two-phaseapproach for model learning suffers from intensive computation burden and unreliableevaluation. To tackle these problems, we adopt Bayesian Ying-Yang (BYY) harmonylearning that has automatic model selection ability during parameter estimation.Experimental results illustrate that the BYY learning can significantly decrease thecomputation burden while improving the accuracy of parameter estimation and modelselection.
6. The last part demonstrates the feasibility of recognition using complex HRRP. Based on the theoretical analysis, it is pointed out that the variation of initial-phase hasno effect on the statistical distribution of HRRP. So it is natural to generalize the FAmodel to the complex domain and model complex HRRP directly. By using additionalphase information in complex HRRP, the complex FA (CFA) model is superior to the FAmodel in recognition performance. Also, we develop a noise adaptive modificationalgorithm for the CFA model. Since there is no approximation made in the modificationstep, this algorithm can gain better performance under low SNR conditions.
引文
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