基于信号模型的SAR参数化成像技术研究
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摘要
合成孔径雷达（Synthetic Aperture Radar，SAR）是一种高分辨率的成像雷达系统，它利用雷达平台与地面观测区之间的相对运动来形成大的方位孔径，进而生成高方位分辨率的图像，实现全天候、全天时对地观测成像。
传统的SAR成像处理算法根据雷达平台和目标之间的距离变化关系，分析回波信号的相位变化，并利用匹配滤波等信号处理技术实现对目标回波信号的聚焦。随着SAR技术的不断发展，其应用领域也越来越广泛，对SAR成像信号处理提出了更高的要求。
首先，需要精确的运动补偿算法和平台运动参数测量手段。常用的SAR成像处理算法基于雷达平台和成像目标之间的几何模型，该模型对平台实际运动描述的精准程度直接影响最终生成的SAR图像的聚焦效果和质量。利用传感器测量平台运动参数以及基于回波数据的参数估计和运动补偿技术，能有效弥补几何模型在描述平台实际运动上的不足，聚焦生成高质量的SAR图像。这一点对高分辨率、非稳定平台条件下的聚焦成像处理尤为重要。
其次，需要高分辨率SAR成像处理算法。雷达分辨率与信号带宽直接相关，通常可以通过大带宽的发射信号来提高距离向分辨率，同样地，方位向高分辨率可以通过增加方位向多普勒带宽来实现。高分辨率SAR系统更为重要的是获得方位向高分辨率。具体而言，更短的发射波长，更长的合成孔径时间等手段，都有利于提高方位向分辨率。但是，增大合成孔径时间会带来更大的距离徙动，高分辨率SAR成像处理算法必须解决这一问题。
再次，需要研究非稳定、高机动平台和考虑轨道与地球表面弯曲的星载平台SAR成像处理。与常规SAR运载平台相比，小型平台的飞行轨迹与运动规律更加复杂，并且易于受外界扰动的影响。此外，由于小型飞行平台难以安装高精度运动参数测量系统，无法实现平台运动参数测量。对于导弹等高机动平台，出于战略和战术要求考虑，平台需要做机动飞行，对高精度SAR成像处理是很大的挑战。另外，在某些特定场合星载SAR成像处理中，必须考虑卫星轨道弯曲和地球表面曲率，比如地球同步轨道合成孔径雷达（Geosynchronous SAR,GEOSAR）。
最后，需要研究成像目标特性。这里所说的目标特性包括散射机理和运动参数。从某种意义上来说，SAR图像可以理解为观测区域内地面雷达散射系数的映射，即地面观测区域散射系数经雷达接收系统和信号处理系统冲击响应最终生成SAR图像。但是，传统SAR成像算法很少涉及对成像目标散射机理的研究，SAR数据聚焦过程不考虑目标散射对聚焦和图像质量的影响。所以，现在的SAR成像算法以及成像产品无法提供目标散射特征等原理性信息，不能实现真正意义上的SAR图像解译。另外，SAR数据中包含的运动目标回波与静止目标回波有完全不同的聚焦参数需求，统一的、一体化的成像算法很难实现运动目标和静止目标同时成像。
非稳定、高机动平台条件下的成像处理对成像算法提出更高的要求，尤其在高分辨率成像处理中更加突出。同时，成像处理中不考虑目标特性，不仅增加后续解译的工作量，更为严重的是会导致目标细节的丢失。本文将研究目标回波信号模型，依据该信号模型利用参数估计的手段从回波信号中提取各目标信号成分，并对各信号成分分别实现聚焦。创新性的工作主要体现在如下几个方面。
第一，提出了基于信号模型的SAR参数化成像技术。该处理技术不依赖于雷达平台与目标的相对位置关系，可以实现在对平台运动信息未知情况下的目标聚焦处理。基于信号模型的SAR参数化成像处理技术以模型所能表示的目标作为最小成像单元，把成像区内所有成像单元用模型参数的形式来加以刻画，这样，不仅能实现不同成像单元按各自参数分别聚焦，还为实现信号级目标特征提取和识别提供了一种技术途径。
第二，作为基于信号模型的SAR参数化成像处理技术的有效实现手段，给出了两种基于原子分解的SAR成像处理算法。这两种处理算法直接对SAR原始回波进行处理，利用原子分解把回波信号分解成为字典集中原子的组合，并针对各原子成分实现聚焦成像。这不仅能实现对散射点的聚焦成像，还能被应用在背景噪声抑制和感兴趣目标细节增强等方面。
第三，提出了基于信号模型的自适应SAR成像处理算法。该算法的参数估计过程沿散射点距离徙动曲线自适应地进行，通过最大化匹配度函数值来找到该散射点的最优模型参数，并将模型参数投影到等距离-等多普勒网格实现聚焦处理。该算法有很好的聚焦效果，同时能有效地降低旁瓣能量。
最后，作为目标特性研究的探索性工作，本文对回波信号方位向包络估计开展了研究，并提出了一种基于改进连续Chirplet变换（continuous chirplet transform， CCT）的包络估计方法和一种基于原子分解的包络估计方法。
Synthetic aperture radar (SAR) is a high-resolution imaging radarsystem. With the use of the relative motion between the radar platform andthe target region, it can generate high-resolution SAR image in both rangeand azimuth dimensions. It can also achieve all-weather all-time earthobservation and imaging.
Conventional SAR image formation algorithms are developed based onthe data collection geometry between the radar platform and the target.The signal backscattered from the target can be focused by analyzing thesignal phase, furthermore, by using the signal processing techniques suchas matched filtering. With the development of the SAR technique, theapplications of the SAR become wider, and it also results in greatchallenges to the SAR signal processing.
First, precise motion compensation algorithms and motion measurementmethods are needed. Conventional SAR image formation algorithms arebased on the geometry between the radar platform and the target, thequality of the SAR image is directly affected by the deviation between thepresumed geometry and the real one. This geometry deviation can becompensated by the precise motion compensation algorithms and motionmeasurement methods. And then, high-quality well-focused images can beobtained. These two requirements are important, especially in the case ofthe image formation of the high-resolution SAR system or themaneuvering platform.
Second, image formation algorithms are needed for high-resolutionimaging. In general, the radar resolution is determined by the signalbandwidth. The range resolution is determined by the transmitted signalbandwidth, that is, large transmitted signal bandwidth can obtain highrange resolution. The high azimuth resolution can be obtained byincreasing Doppler bandwidth. For the high-resolution SAR system, theazimuth resolution is more important. However, larger azimuth bandwidthwill result in larger range migration, which must be corrected by thehigh-resolution image formation algorithms.
Third, variable motion, orbit curvature and earth curvature must be considered in the image formation. Different from the common SARplatform, the trajectory of the compact platform is more complex, since itis susceptible to external disturbation. It is impossible to provide precisemotion measurement, because it is difficult to mount motion measurementsystem on a compact platform. It is also a great challenge to the imageformation of the maneuvering platform. The orbit curvature and earthcurvature must be considered in spaceborne SAR image formation,especially in the case of the geosynchronous SAR.
Finally, the study on target feature is needed. Here, the study on targetfeature includes the analysis of the scattering mechanism and the targetmotion. The SAR image can be viewed as a projection of the RCS of thetargets in the observed region. That is, the SAR image can be formed byletting the RCS of the observed region through the impulse response of theSAR system. However, target scattering mechanism is overlooked by theconventional SAR image formation algorithms, the effect of the scatteringmechanism to the image formation is not considered. Thus, the SARimage cannot provide the information of the target scattering mechanism,this is adverse to SAR image interpretation. In addition, the focusingparameters of the stationary targets and the moving targets are different,uniform signal processing cannot focus the stationary targets and the moving targets simultaneously.
High maneuvering results in great challenges to imaging processing,especially in the case of high-resolution imaging. Meanwhile, targetfeature is overlooked by the conventional imaging algorithms, this willrequire more efforts to image interpretation, and will also result in themissing of target details. This paper concentrates on the model of thebackscattered signal. With the help of the signal model, the signalcomponents can be distinguished by the parameter estimation techniques,each component can then be focused separately according to its ownparameter(s). The achievements are described as follows.
First, the model-based parametric SAR image formation technique isproposed. This technique does not require explicit knowledge of the datacollection geometry, it can focus the target even when the platform motionis unknown. The target that can be described by the model is treated as theminimum imaging unit, each unit in the imaging region can becharacterized by the model parameters. Thus, each unit can be focusedprogressively. This technique also provides an approach to target featureextraction and recognition from a signal-level point of view.
Second, the atomic decomposition-based image formation algorithm isproposed. It is a realization of the model-based parametric SAR image formation technique. Atomic decomposition-based image formationalgorithm can focus the scatterers, and it can be also useful in the case ofbackground noise suppression and highlighting the target of interest.
Third, the model-based adaptive SAR image formation algorithm isproposed. The model parameters can be estimated along the rangemigration curve adaptively, the parameters of each scatterer can beobtained by maximizing the cost function. The final SAR image isgenerated by projecting the estimated parameters to the grid formed by theiso-range and iso-Doppler lines. The scatterers can be well focused byusing this algorithm, and the sidelobe of the impulse response can also bereduced.
Finally, the effect of the target-induced azimuth envelope to the imageformation is discussed, an improved CCT-based envelope estimationalgorithm and an atomic decomposition-based envelope estimationalgorithm are proposed.
传统的SAR成像处理算法根据雷达平台和目标之间的距离变化关系，分析回波信号的相位变化，并利用匹配滤波等信号处理技术实现对目标回波信号的聚焦。随着SAR技术的不断发展，其应用领域也越来越广泛，对SAR成像信号处理提出了更高的要求。
首先，需要精确的运动补偿算法和平台运动参数测量手段。常用的SAR成像处理算法基于雷达平台和成像目标之间的几何模型，该模型对平台实际运动描述的精准程度直接影响最终生成的SAR图像的聚焦效果和质量。利用传感器测量平台运动参数以及基于回波数据的参数估计和运动补偿技术，能有效弥补几何模型在描述平台实际运动上的不足，聚焦生成高质量的SAR图像。这一点对高分辨率、非稳定平台条件下的聚焦成像处理尤为重要。
其次，需要高分辨率SAR成像处理算法。雷达分辨率与信号带宽直接相关，通常可以通过大带宽的发射信号来提高距离向分辨率，同样地，方位向高分辨率可以通过增加方位向多普勒带宽来实现。高分辨率SAR系统更为重要的是获得方位向高分辨率。具体而言，更短的发射波长，更长的合成孔径时间等手段，都有利于提高方位向分辨率。但是，增大合成孔径时间会带来更大的距离徙动，高分辨率SAR成像处理算法必须解决这一问题。
再次，需要研究非稳定、高机动平台和考虑轨道与地球表面弯曲的星载平台SAR成像处理。与常规SAR运载平台相比，小型平台的飞行轨迹与运动规律更加复杂，并且易于受外界扰动的影响。此外，由于小型飞行平台难以安装高精度运动参数测量系统，无法实现平台运动参数测量。对于导弹等高机动平台，出于战略和战术要求考虑，平台需要做机动飞行，对高精度SAR成像处理是很大的挑战。另外，在某些特定场合星载SAR成像处理中，必须考虑卫星轨道弯曲和地球表面曲率，比如地球同步轨道合成孔径雷达（Geosynchronous SAR,GEOSAR）。
最后，需要研究成像目标特性。这里所说的目标特性包括散射机理和运动参数。从某种意义上来说，SAR图像可以理解为观测区域内地面雷达散射系数的映射，即地面观测区域散射系数经雷达接收系统和信号处理系统冲击响应最终生成SAR图像。但是，传统SAR成像算法很少涉及对成像目标散射机理的研究，SAR数据聚焦过程不考虑目标散射对聚焦和图像质量的影响。所以，现在的SAR成像算法以及成像产品无法提供目标散射特征等原理性信息，不能实现真正意义上的SAR图像解译。另外，SAR数据中包含的运动目标回波与静止目标回波有完全不同的聚焦参数需求，统一的、一体化的成像算法很难实现运动目标和静止目标同时成像。
非稳定、高机动平台条件下的成像处理对成像算法提出更高的要求，尤其在高分辨率成像处理中更加突出。同时，成像处理中不考虑目标特性，不仅增加后续解译的工作量，更为严重的是会导致目标细节的丢失。本文将研究目标回波信号模型，依据该信号模型利用参数估计的手段从回波信号中提取各目标信号成分，并对各信号成分分别实现聚焦。创新性的工作主要体现在如下几个方面。
第一，提出了基于信号模型的SAR参数化成像技术。该处理技术不依赖于雷达平台与目标的相对位置关系，可以实现在对平台运动信息未知情况下的目标聚焦处理。基于信号模型的SAR参数化成像处理技术以模型所能表示的目标作为最小成像单元，把成像区内所有成像单元用模型参数的形式来加以刻画，这样，不仅能实现不同成像单元按各自参数分别聚焦，还为实现信号级目标特征提取和识别提供了一种技术途径。
第二，作为基于信号模型的SAR参数化成像处理技术的有效实现手段，给出了两种基于原子分解的SAR成像处理算法。这两种处理算法直接对SAR原始回波进行处理，利用原子分解把回波信号分解成为字典集中原子的组合，并针对各原子成分实现聚焦成像。这不仅能实现对散射点的聚焦成像，还能被应用在背景噪声抑制和感兴趣目标细节增强等方面。
第三，提出了基于信号模型的自适应SAR成像处理算法。该算法的参数估计过程沿散射点距离徙动曲线自适应地进行，通过最大化匹配度函数值来找到该散射点的最优模型参数，并将模型参数投影到等距离-等多普勒网格实现聚焦处理。该算法有很好的聚焦效果，同时能有效地降低旁瓣能量。
最后，作为目标特性研究的探索性工作，本文对回波信号方位向包络估计开展了研究，并提出了一种基于改进连续Chirplet变换（continuous chirplet transform， CCT）的包络估计方法和一种基于原子分解的包络估计方法。
Synthetic aperture radar (SAR) is a high-resolution imaging radarsystem. With the use of the relative motion between the radar platform andthe target region, it can generate high-resolution SAR image in both rangeand azimuth dimensions. It can also achieve all-weather all-time earthobservation and imaging.
Conventional SAR image formation algorithms are developed based onthe data collection geometry between the radar platform and the target.The signal backscattered from the target can be focused by analyzing thesignal phase, furthermore, by using the signal processing techniques suchas matched filtering. With the development of the SAR technique, theapplications of the SAR become wider, and it also results in greatchallenges to the SAR signal processing.
First, precise motion compensation algorithms and motion measurementmethods are needed. Conventional SAR image formation algorithms arebased on the geometry between the radar platform and the target, thequality of the SAR image is directly affected by the deviation between thepresumed geometry and the real one. This geometry deviation can becompensated by the precise motion compensation algorithms and motionmeasurement methods. And then, high-quality well-focused images can beobtained. These two requirements are important, especially in the case ofthe image formation of the high-resolution SAR system or themaneuvering platform.
Second, image formation algorithms are needed for high-resolutionimaging. In general, the radar resolution is determined by the signalbandwidth. The range resolution is determined by the transmitted signalbandwidth, that is, large transmitted signal bandwidth can obtain highrange resolution. The high azimuth resolution can be obtained byincreasing Doppler bandwidth. For the high-resolution SAR system, theazimuth resolution is more important. However, larger azimuth bandwidthwill result in larger range migration, which must be corrected by thehigh-resolution image formation algorithms.
Third, variable motion, orbit curvature and earth curvature must be considered in the image formation. Different from the common SARplatform, the trajectory of the compact platform is more complex, since itis susceptible to external disturbation. It is impossible to provide precisemotion measurement, because it is difficult to mount motion measurementsystem on a compact platform. It is also a great challenge to the imageformation of the maneuvering platform. The orbit curvature and earthcurvature must be considered in spaceborne SAR image formation,especially in the case of the geosynchronous SAR.
Finally, the study on target feature is needed. Here, the study on targetfeature includes the analysis of the scattering mechanism and the targetmotion. The SAR image can be viewed as a projection of the RCS of thetargets in the observed region. That is, the SAR image can be formed byletting the RCS of the observed region through the impulse response of theSAR system. However, target scattering mechanism is overlooked by theconventional SAR image formation algorithms, the effect of the scatteringmechanism to the image formation is not considered. Thus, the SARimage cannot provide the information of the target scattering mechanism,this is adverse to SAR image interpretation. In addition, the focusingparameters of the stationary targets and the moving targets are different,uniform signal processing cannot focus the stationary targets and the moving targets simultaneously.
High maneuvering results in great challenges to imaging processing,especially in the case of high-resolution imaging. Meanwhile, targetfeature is overlooked by the conventional imaging algorithms, this willrequire more efforts to image interpretation, and will also result in themissing of target details. This paper concentrates on the model of thebackscattered signal. With the help of the signal model, the signalcomponents can be distinguished by the parameter estimation techniques,each component can then be focused separately according to its ownparameter(s). The achievements are described as follows.
First, the model-based parametric SAR image formation technique isproposed. This technique does not require explicit knowledge of the datacollection geometry, it can focus the target even when the platform motionis unknown. The target that can be described by the model is treated as theminimum imaging unit, each unit in the imaging region can becharacterized by the model parameters. Thus, each unit can be focusedprogressively. This technique also provides an approach to target featureextraction and recognition from a signal-level point of view.
Second, the atomic decomposition-based image formation algorithm isproposed. It is a realization of the model-based parametric SAR image formation technique. Atomic decomposition-based image formationalgorithm can focus the scatterers, and it can be also useful in the case ofbackground noise suppression and highlighting the target of interest.
Third, the model-based adaptive SAR image formation algorithm isproposed. The model parameters can be estimated along the rangemigration curve adaptively, the parameters of each scatterer can beobtained by maximizing the cost function. The final SAR image isgenerated by projecting the estimated parameters to the grid formed by theiso-range and iso-Doppler lines. The scatterers can be well focused byusing this algorithm, and the sidelobe of the impulse response can also bereduced.
Finally, the effect of the target-induced azimuth envelope to the imageformation is discussed, an improved CCT-based envelope estimationalgorithm and an atomic decomposition-based envelope estimationalgorithm are proposed.
引文
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