摘要
当观测数据中含有粗差(异常值)时,其误差分布可视为污染分布。卡尔曼滤波对异常值非常敏感,它严重影响动态定位的精度。本文应用Bayes定理,给出测量噪声为污染正态分布时的一种Bayes估计动态模型,通过模拟和实例计算与比较分析,表明算法的有效性。
The observations are sometimes contaminated by gross errors （or outliers） and the error distribution in this case can be regarded as contaminated distribution. The precision of kinematic positioning by usingKalman filtering is very sensitive to theoutliers. A Bayesian estimator is given for dynamic models with contaminated distribution, based on Bayesian theory. Simulation and practical examples of filtering are given, which show that the modified Kalman filtering is effective and reliable.
出处
《测绘学报》
EI
CSCD
北大核心
2009年第2期138-143,151,共7页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(40874005,40774001)
863专项基金(2007AA12Z331)
教育部博士点基金(200805331086)
关键词
卡尔曼滤波
抗差滤波
Boyes估计
动态定位
Kalman filtering
Robust filtering Bayes estimation
Kinematic positioning
