换热反问题输入数据的正则预处理及其对解的影响

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多帧PML规整化湍流退化图像复原算法

从湍流退化图像中有效地恢复出原目标图像,是空间目标成像观测亟待解决的问题。为解决强模糊实际观测湍流退化图像的复原问题,本文提出了多帧PML规整化图像复原算法。该算法主要利用非线性规整化项在点扩散函数和目标图像的估计过程中进行平滑。为验证算法的有效性,针对强噪声退化图像进行了恢复实验,实验结果证明,该算法对于强噪声湍流退化图像的复原是非常有效的。     

Bayesian approach to retrieval of vertical ozone profile from radiometry data

A technique for retrieving vertical distributions (profiles) of atmospheric gas constituents from data of passive remote sensing of the atmosphere is proposed. The goal of the technique based on the statistical (Bayesian) approach to solution of inverse problems is construction of probability distribution for a sought quantity throughout the interval of the studied heights. It is assumed that initial data contain measurement noise, and a priori information about properties of the profile is used. It is proposed to approximate the sought profile by a function in the form of an artificial neural network. This approximation allows optimal inclusion of a priori information into retrieval procedure, thus ensuring the most effective regularization of the problem. Efficiency of the proposed technique is demonstrated on an example of retrieval of vertical ozone profile from data of ground-based sounding of the atmosphere in the millimeter wavelength range. Results of profile retrieval from model data and from spectra of radiation temperature of the atmosphere measured in the Apatity (67° N, 33° E) in the winter of 2002–2003 are presented.     

Regional gravity field model in Pakistan area from the combination of CHAMP,GRACE and ground data using least squares collocation: A case study

This study describes a methodology of recovery of the Earth’s gravity field from CHAMP and GRACE satellites data in Pakistan using least squares collocation (LSC) based downward continuation technique. The CHAMP height anomalies and GRACE gravity disturbances derived from the observed satellite data have been used in combination solution using LSC with observed gravity values at the Earth surface. The combined covariance functions of height anomalies and/or gravity disturbances at satellite altitudes and observed gravity anomalies at Earth surface have been used as the basis for combination and downward continuation solution. The variance of predicted gravity anomalies from GRACE gravity disturbances is relatively lower than the corresponding results of gravity anomalies from CHAMP height anomalies. This fact may be attributed partly to the amplification of noise and partly to the unstable inverse transformation process of height anomalies to gravity anomalies. The impact of data error variance has been studied in the context of smoothing and noise reduction in the final solution of downward continuation using least squares collocation. The raising of data error suppresses the noise and as a result a smooth final solution is obtained. The prediction results appear to be dependent on the quality of data and goodness of combined covariance function, which are fairly comparable for the CHAMP and GRACE data. The recovered gravity field from satellite data appears to contribute mainly to medium and long wavelength parts of total gravity field spectrum. Due to flexibility of data handling in least squares collocation, this procedure is applicable to any observable of gravity field being at different altitudes and with different data spacing.     

用于表面温度测量的导热反问题的误差研究

讨论了用于温度测量的非稳态一维线性导热反问题。通过引入正则参数 ,这种不适定问题得到了稳定的解。在给出非级数形式的分析解后 ,计算误差可以通过数值解与准确解的比较确定。同时讨论了影响计算误差的各种因素。     

基于远场模式的散射体形状成像

对线性抽样法的电磁场逆散射求解进行了研究。为避免迭代和优化法的正问题求解，用积分算子将散射物边界数据映射到散射场的远场模式，针对第一类积分方程的不适定性，用Tikhonov正则化法求解，并用高阶收敛算法确定正则化参数以快速数值实现。数值计算结果表明：线性抽样法＋确定正则化参数的高阶收敛算法可较好地滤除噪声，减少了确定正则化参数的计算量。     

模型的稀疏选择与参数辨识及应用

基于正则化技术的稀疏成分分析方法可同时完成模型选择和参数估计功能。现分别从迭代算法的设计和对应岭估计的构造两方面切入,研究正则化函数的选取及特点,并深入分析其模型参数辨识的机理,说明正则化参数与广义岭估计的最佳岭参数的耦合性。该方法可操作性强,可保证参数迭代的收敛性,对于正则化函数的构造和参数估计的统计特性分析均有规则可循。缺项多项式和制导工具误差系数求解的数值例子表明,该方法具有有效的一体化模型选择和参数估计功能。     

基于各向异性和非线性正则化的湍流退化图像复原

提出了一种基于各向异性和非线性正则化的湍流退化图像复原新算法。为了有效地从湍流退化图像中估计出退化模型，在湍流点扩展函数的优化估计过程中合理地融合了有关湍流点扩展函数的一些基本的先验知识。首先，将点扩展函数的非负性和光滑性约束加入到目标函数中。再针对湍流点扩展函数的衰减性质，建立了一个具有非线性和空间各向异性的正则化函数，使其在重建点扩展函数时能适当地进行空间梯度平滑。由此通过迭代极小化目标函数来优化估计湍流点扩展函数值，进而恢复图像。对各种噪声条件下的湍流退化图像进行了恢复实验，实验结果表明本文算法具有较强的稳定性和抗噪能力。     

单帧数字图像分辨率改善的频域正则化方法

现有多帧序列频域分辨率改善方法主要基于柯西-高斯先验模型，对单帧图像处理效果不理想。在分析频谱混叠公式的基础上，研究了单帧图像分辨率改善的频域方法。提出了基于柯西-高斯先验模型的正则化方法，由于柯西-高斯先验模型具有较好的信号稀疏表示能力，因此能有效地改善图像的分辨率。设计了迭代求解算法，给出了迭代初值的稳健算法，并将二维计算问题转化为一维计算问题，实现了方法的快速计算，同时大大地减少了存储量。文中给出了一维信号和遥感图像两个算例，计算结果表明，方法可以有效地改善图像的分辨率。     

基于lk范数正则化方法的SAR图像超分辨

提高合成孔径雷达（SAR）图像的分辨率对自动目标识别等具有重要意义。为此改进了一种基于lk范数正则化方法，并用于SAR图像超分辨。该方法通过合理开发利用符合SAR成像工程背景的先验知识，构造附加约束，把图像超分辨问题规划为形式简单的带约束优化问题。仿真和实测数据计算结果证实了该方法的有效性。