阵列雷达SAR-GMTI关键技术研究
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摘要
合成孔径雷达(SAR)是现代雷达的一项突破性成就。它具有全天候、全天时和远距离成像的特点,能够从空中实时提供类似光学照片的二维地物地貌图像并且具有良好的空间分辨能力和辐射测量保真度,它的优良性能使它在军事和民用上都得到非常广泛的应用。SAR的初衷是针对地面静止场景成像而发展起来的,在常规的SAR图像中,地面运动目标图像会出现距离走动、散焦、方位位置偏移等现象,动目标往往只能以模糊的形式叠加在静止场景SAR图像上。在实际的应用中,特别是在军事的应用上,人们总是希望通过SAR图像来进行地面动目标检测(GMTI)并将其标注在场景的SAR图像上,SAR-GMTI技术就这样应运而生了。多通道SAR比单通道SAR能够提供更多的系统自由度。就实现GMTI功能而言,雷达阵列沿航向直线分布为最优构型,可以获得最优的杂波抑制性能,能够在地杂波或者海杂波中清楚地区分出运动目标并且能够正确地估计出其运动参数,因而被广泛地应用于战场感知和侦察中。
目前世界上许多国家都在大力研究基于SAR的多通道地面运动目标检测和定位技术,努力寻求各种高效、实用的动目标检测方法。本文在深入分析国内外SAR-GMTI研究成果的基础上,对合成孔径雷达动目标检测和成像机理进行了较为详细的研究和分析,重点研究了沿航迹正侧视阵列雷达实现SAR-GMTI时所需解决的通道均衡、杂波抑制以及运动目标参数估计等问题。论文的主要内容可概括为以下四个部分:
1.针对多通道均衡问题,提出了一种对强目标信号污染稳健的自适应通道盲均衡算法。该方法在获得SAR图像的基础上,根据功率挑选准则选取数据样本矢量,并对其模进行归一化处理,对模归一化矢量的协方差矩阵进行特征分解后取其最大特征值对应的特征矢量,最后再将原始数据矢量的各分量除以该特征矢量的对应分量,从而达到均衡通道幅相误差的目的。理论分析、仿真实验和某机载实测数据的处理结果表明该方法能够有效地均衡通道响应误差,对强干扰或目标信号的污染具有稳健性。
2.针对图像配准误差严重影响传统GMTI算法的目标检测性能的问题,提出了SAR图像域基于联合相关导向矢量模型的自适应目标检测及测速定位方法。该方法在很大程度上增加了系统的自由度,大大提高了系统的杂波抑制性能。仿真实验表明该方法对图像配准误差具有较强的稳健性,在图像配准误差达到一个像素的情况下仍能获得良好的目标检测性能和较高的测速定位精度。
3.提出了一种稳健的特征子空间投影杂波抑制方法。在理想情况下,传统的特征子空间投影方法具有良好的杂波抑制性能,然而在实际中存在强干扰或目标信号污染时,其杂波抑制性能会受到严重的影响。为此,本文提出了一种新的特征子空间投影方法,理论分析表明该方法对强干扰或目标信号污染具有稳健性。仿真实验和实测数据处理验证了方法的有效性。
4.提出了一种基于模归一化矢量的自适应波束形成方法。该方法在杂波抑制和目标检测基础上实现SAR-GMTI中的目标定位。它首先利用次优权进行杂波抑制,然后针对目标所在的位置利用最优权进行目标径向速度搜索运算,根据输出信杂噪比最大准则估计目标径向速度,最后在SAR图像上对目标的真实位置进行标定。该方法有效降低了目标信号污染的影响。仿真实验和机载实测数据处理结果验证了该方法的有效性和稳健性。
Synthetic aperture radar (SAR) is a great achievement of modern radar. Like various optical sensors, SAR can provide a two-dimensional image of a scene with good spatial resolution and high precision of radiation measurement, with the advantages of all-weather working, and long-range surveillance. Therefore, SAR is of great value for both civilian and military applications. Traditional SAR radars simply take the“picture”of a stationary scene. If moving targets exist, however, the SAR image will become blurred. A moving target with radial velocity on the SAR image will exhibit displaced from its true position, and the motion in the azimuth direction will cause its image to be dispersed along azimuth. In practical applications, especially in military application, it is always expected to achieve ground moving target indication (GMTI) by using SAR sensors and to relocate ground moving targets on the SAR image of the scene. This technique is called SAR-GMTI. Since along-track multi-channel SAR can provide more degrees of freedom than single channel one, along-track multi-channel SAR, as the optimal system geometry, provides the powerful ability to suppress the land/sea clutter for target detection and parameter estimation. Thus it has been used extensively for air-to-ground surveillance and reconnaissance purposes.
In recent years, many countries around the world have been making great effort to develop spaceborne/airborne radar for GMTI based on multi-channel SAR, explore new SAR-GMTI technologies and design highly efficient detection and location algorithms. Based on the extensive investigation of the airborne SAR-GMTI, this thesis presents SAR-GMTI theory and investigates moving target detection, parameter estimation and imaging approaches. We deal mainly with channel equalization, clutter suppression and moving parameter estimation in SAR-GMTI for along-track side-looking array radar.
The main work of the thesis is summarized as follows:
1. To deal with channel equalization, an adaptive blind channel equalization approach, which can mitigate the influence of strong target signals or interferences contamination, is proposed for multi-channel SAR-GMTI system. Based on the obtained SAR images, firstly, the method chooses pixel data vectors with relatively strong power and enables these vectors modulus-normalized. Secondly, these modulus-normalized vectors are used to construct the covariance matrix and then the eigen-decomposition of the covariance matrix is performed. The eigenvector associated with the largest eigenvalue contains the information regarding the channel imbalance. Finally, the channel imbalance is corrected by dividing each element of the original pixel data vector by the corresponding element of the eigenvector associated with the maximum eigenvalue. The validity and robustness of the proposed approach are confirmed by theoretical analysis and the real data processing.
2. For the multi-channel SAR-GMTI system, the presence of large image coregistration errors will have serious influence on the performance of the clutter suppression and the precision of parameter estimation. To deal with the problem, a new adaptive approach based on joint correlation steering vector for moving target detection is proposed. The method, in general, can increase the degrees of freedom of adaptive processing, and as a result, its performance of clutter suppression is greatly improved. It has good robustness to image coregistration errors, and can provide accurate estimates of the radial velocities of ground moving targets. The validity and superiority of this method are verified by the simulated data.
3. A robust eigen-subspace projection approach to clutter suppression is proposed for a multi-channel SAR system. In an ideal environment, the conventional eigen-subspace projection method can offer a perfect performance of clutter suppression. In practice, however, it suffers seriously from strong target signals or interferences contamination. To deal with this problem, a new eigen-subspace projection method is presented. Performance analysis shows that the method is robust to strong target signals or interferences contamination. Simulation results and the real data processing confirm the validity of the method.
4. An adaptive beamforming method based on modulus-normalized vector is proposed. It uses the suboptimal weight vector to suppress clutter and then searches target radial velocity at the detected position of the target using the optimal weight vector. The radial velocity can be estimated according to the criterion of the maximum signal-to-clutter plus noise ratio output. Finally, the moving target can be correctly relocated on the SAR image according to the estimated radial velocity. This method can mitigate the influence of target signals or interferences contamination. Simulation results and the airborne radar real data processing confirm the validity and robustness of the proposed method.
目前世界上许多国家都在大力研究基于SAR的多通道地面运动目标检测和定位技术,努力寻求各种高效、实用的动目标检测方法。本文在深入分析国内外SAR-GMTI研究成果的基础上,对合成孔径雷达动目标检测和成像机理进行了较为详细的研究和分析,重点研究了沿航迹正侧视阵列雷达实现SAR-GMTI时所需解决的通道均衡、杂波抑制以及运动目标参数估计等问题。论文的主要内容可概括为以下四个部分:
1.针对多通道均衡问题,提出了一种对强目标信号污染稳健的自适应通道盲均衡算法。该方法在获得SAR图像的基础上,根据功率挑选准则选取数据样本矢量,并对其模进行归一化处理,对模归一化矢量的协方差矩阵进行特征分解后取其最大特征值对应的特征矢量,最后再将原始数据矢量的各分量除以该特征矢量的对应分量,从而达到均衡通道幅相误差的目的。理论分析、仿真实验和某机载实测数据的处理结果表明该方法能够有效地均衡通道响应误差,对强干扰或目标信号的污染具有稳健性。
2.针对图像配准误差严重影响传统GMTI算法的目标检测性能的问题,提出了SAR图像域基于联合相关导向矢量模型的自适应目标检测及测速定位方法。该方法在很大程度上增加了系统的自由度,大大提高了系统的杂波抑制性能。仿真实验表明该方法对图像配准误差具有较强的稳健性,在图像配准误差达到一个像素的情况下仍能获得良好的目标检测性能和较高的测速定位精度。
3.提出了一种稳健的特征子空间投影杂波抑制方法。在理想情况下,传统的特征子空间投影方法具有良好的杂波抑制性能,然而在实际中存在强干扰或目标信号污染时,其杂波抑制性能会受到严重的影响。为此,本文提出了一种新的特征子空间投影方法,理论分析表明该方法对强干扰或目标信号污染具有稳健性。仿真实验和实测数据处理验证了方法的有效性。
4.提出了一种基于模归一化矢量的自适应波束形成方法。该方法在杂波抑制和目标检测基础上实现SAR-GMTI中的目标定位。它首先利用次优权进行杂波抑制,然后针对目标所在的位置利用最优权进行目标径向速度搜索运算,根据输出信杂噪比最大准则估计目标径向速度,最后在SAR图像上对目标的真实位置进行标定。该方法有效降低了目标信号污染的影响。仿真实验和机载实测数据处理结果验证了该方法的有效性和稳健性。
Synthetic aperture radar (SAR) is a great achievement of modern radar. Like various optical sensors, SAR can provide a two-dimensional image of a scene with good spatial resolution and high precision of radiation measurement, with the advantages of all-weather working, and long-range surveillance. Therefore, SAR is of great value for both civilian and military applications. Traditional SAR radars simply take the“picture”of a stationary scene. If moving targets exist, however, the SAR image will become blurred. A moving target with radial velocity on the SAR image will exhibit displaced from its true position, and the motion in the azimuth direction will cause its image to be dispersed along azimuth. In practical applications, especially in military application, it is always expected to achieve ground moving target indication (GMTI) by using SAR sensors and to relocate ground moving targets on the SAR image of the scene. This technique is called SAR-GMTI. Since along-track multi-channel SAR can provide more degrees of freedom than single channel one, along-track multi-channel SAR, as the optimal system geometry, provides the powerful ability to suppress the land/sea clutter for target detection and parameter estimation. Thus it has been used extensively for air-to-ground surveillance and reconnaissance purposes.
In recent years, many countries around the world have been making great effort to develop spaceborne/airborne radar for GMTI based on multi-channel SAR, explore new SAR-GMTI technologies and design highly efficient detection and location algorithms. Based on the extensive investigation of the airborne SAR-GMTI, this thesis presents SAR-GMTI theory and investigates moving target detection, parameter estimation and imaging approaches. We deal mainly with channel equalization, clutter suppression and moving parameter estimation in SAR-GMTI for along-track side-looking array radar.
The main work of the thesis is summarized as follows:
1. To deal with channel equalization, an adaptive blind channel equalization approach, which can mitigate the influence of strong target signals or interferences contamination, is proposed for multi-channel SAR-GMTI system. Based on the obtained SAR images, firstly, the method chooses pixel data vectors with relatively strong power and enables these vectors modulus-normalized. Secondly, these modulus-normalized vectors are used to construct the covariance matrix and then the eigen-decomposition of the covariance matrix is performed. The eigenvector associated with the largest eigenvalue contains the information regarding the channel imbalance. Finally, the channel imbalance is corrected by dividing each element of the original pixel data vector by the corresponding element of the eigenvector associated with the maximum eigenvalue. The validity and robustness of the proposed approach are confirmed by theoretical analysis and the real data processing.
2. For the multi-channel SAR-GMTI system, the presence of large image coregistration errors will have serious influence on the performance of the clutter suppression and the precision of parameter estimation. To deal with the problem, a new adaptive approach based on joint correlation steering vector for moving target detection is proposed. The method, in general, can increase the degrees of freedom of adaptive processing, and as a result, its performance of clutter suppression is greatly improved. It has good robustness to image coregistration errors, and can provide accurate estimates of the radial velocities of ground moving targets. The validity and superiority of this method are verified by the simulated data.
3. A robust eigen-subspace projection approach to clutter suppression is proposed for a multi-channel SAR system. In an ideal environment, the conventional eigen-subspace projection method can offer a perfect performance of clutter suppression. In practice, however, it suffers seriously from strong target signals or interferences contamination. To deal with this problem, a new eigen-subspace projection method is presented. Performance analysis shows that the method is robust to strong target signals or interferences contamination. Simulation results and the real data processing confirm the validity of the method.
4. An adaptive beamforming method based on modulus-normalized vector is proposed. It uses the suboptimal weight vector to suppress clutter and then searches target radial velocity at the detected position of the target using the optimal weight vector. The radial velocity can be estimated according to the criterion of the maximum signal-to-clutter plus noise ratio output. Finally, the moving target can be correctly relocated on the SAR image according to the estimated radial velocity. This method can mitigate the influence of target signals or interferences contamination. Simulation results and the airborne radar real data processing confirm the validity and robustness of the proposed method.
引文
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