非高斯/非线性滤波理论及其在GNSS动态定位中的应用

项目名称： 非高斯/非线性滤波理论及其在GNSS动态定位中的应用

项目编号： No.41274016

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 天文学、地球科学

项目作者： 隋立芬

作者单位： 中国人民解放军信息工程大学

项目金额： 70万元

中文摘要： 针对GNSS动态导航定位和组合导航解算中非高斯、非线性随机系统问题，将现代时间序列分析法和现代统计学习理论、以及现有的有色噪声估计理论、自适应估计理论综合应用于非高斯/非线性的函数模型和随机模型的实时构模与参数估计，提出实用的动态非线性滤波模型非线性强度的度量方法，建立评价非线性滤波性能的实用准则，研究噪声的分布和相关性度量与探测的有效方法，提出基于时间序列模型补偿和核半参数模型补偿的自适应滤波新算法，设计适用于强非线性、非高斯噪声系统模型的高斯混合ARMA模型滤波器，建立一套能同时控制异常和有色噪声及线性化模型误差的影响、适合于GNSS动态导航定位的非高斯/非线性随机系统的滤波理论和实用算法，为动态导航定位及相关领域的应用提供严密的科学理论和行之有效的技术手段。本研究对航空航天重力测量、海洋动态载体的实时导航定位、目标跟踪定位、航天器精密定轨等领域的数据处理具有重要的理论意义和实用价值。

中文关键词： GNSS导航定位；组合导航；有色噪声；高斯混合模型；非线性滤波

英文摘要： For the non-Gaussian and non-linear problem of stochastic system both in GNSS and INS navigation and kinematic positioning, in view of concrete problem and condition in practical GNSS application, modern time series analysis, modern statistic learning theory, colored noise filtering and adaptive estimation are synthetically used in real-time modeling and parameter estimating of the function model, which is non-linear, and stochastic model, which is non-Gaussian. A practicable method for measuring the non-linearity intensity of kinematic non-linear filtering model is introduced and an evaluation criteria based on three capabilities of non-linear filtering, accuracy, conformance and confidence, is established. The methods for measuring distribution character and detecting correlation of noise are studied. Considering the more complicated problem of non-Gaussian colored noise, a new adaptive filtering algorithm using time series and kernel semi-parametric model compensation is proposed. Gaussian mixture ARMA filtering for system with serious non-linearity and non-Gaussian noise is devised. For the pivotal issue of parameter estimation in Gaussian mixture model, a practicable efficient adaptive Gaussian mixture EKF algorithm is researched and employed in data processing of non-linear system with non-Gaussian color

英文关键词： GNSS navigation and positioning；GNSS/INS integrated navigation；Colored noise；Gaussian mixture model；Nonlinear filtering