测量噪声污染模型下的动态定位Bayes算法

基金项目: 国家自然科学基金资助项目(40874005,40774001)；国家863计划资助项目(2007AA12Z331)；国家教育部博士点专项基金资助项目(200805331086)

详细信息

Bayesian Estimation for Kinematic Positioning with Contaminated Model of Measurement Noise

¹ School of Info-Physics and Geomatics Engineering,Central South University,South Lushan Road,Changsha 410083,China; ² Xi'an Research Institute of Surveying and Mapping,1 Middle Yanta Road,Xi'an 710054,China

Funds: 国家自然科学基金资助项目(40874005,40774001)；国家863计划资助项目(2007AA12Z331)；国家教育部博士点专项基金资助项目(200805331086)

摘要: 应用Bayes定理,给出了测量噪声污染模型下一类动态模型状态的抗差Bayes估计,并同时给出了一种污染率未知时的滤波算法。模拟算例结果证明了该算法的有效性和可靠性。
- 卡尔曼滤波 /
- 粗差 /
- Bayes估计 /
- 动态定位
Abstract: Based on Bayesian theory under contaminated distribution,a robust Bayesian estimator for the state parameters of one kind of dynamic models is given.Meanwhile,we study the algorithms with unknown contaminated factors.A simulation example of filtering shows that the modified Kalman filter is effective and reliable.
- Kalman filtering /
- gross errors /
- Bayes estimation /
- kinematic positioning