如何充分利用partial EIV模型的优点，并分别顾及平差的随机模型不准确、数据含有系统误差、函数模型病态等问题，有效估计模型参数并进行精度评定，是当前测量数据处理领域亟待解决的难题。本项目致力于Partial EIV 模型参数估计理论及其在测量数据处理中的应用研究，将采取两步法简化模型参数的求解，采用赫尔默特方差分量估计和附加相对权比的两种方法处理观测向量和系数矩阵权分配不合理、验前随机模型不准确的问题，利用半参数思想处理系统误差、方差分量估计的虚拟观测法处理模型病态问题，使用比例对称采样策略的UT变换法和自适应MC法进一步把Partial EIV模型参数估值的协方差精度提高至二阶或更高。本项目研究的理论和方法将用于GNSS数据处理、坐标转换、变形监测、数据拟合等测量数据处理问题中。本项目的研究具有重要的理论意义和实用价值，是对现代测量数据处理理论的进一步完善和发展。

How to make full use of advantages of the partial EIV model and considering the problems: inaccuracy of stochastic model, data containing systematic errors, ill-posed function model and so on; how to estimate model parameters effectively and evaluate accuracy are important problems demanding prompt solution. This project is devoted to parameter estimation theory of partial EIV model and its application in surveying data processing. A two steps method will be used to simplify the model parameter estimation. Helmert variance component estimation and the adjustment method derived with weight scaling factor are employed to deal with the problems: (i) the weight matrices of observation and coefficient matrix are unreasonable; (ii) the prior stochastic model contains inaccurate information. The idea of semi-parametric regression will be utilized to process systematic errors and virtual observation method based on variance component estimation will be used to resolve ill-posed problems. Unscented transformation method with scaled symmetric sampling strategy and adaptive Monte Carlo method will be applied to improve approximate accuracy of partial EIV model parameter estimations to second order.The theory and methods of this project will be applied to GNSS data processing, coordinate transformation, deformation monitoring, data-fitting and so on. The objective of this research has great theory significance and utility value which will further consummate and develop modern surveying data processing theory.