通信学报Issue(7)：184-190,7.DOI:10.3969/j.issn.1000-436x.2013.07.021
基于改进当前统计模型的模糊自适应车辆定位算法
Fuzzy adaptive algorithm based on modified current statistical model for vehicle positioning
摘要
Abstract
The singer model and current statistical model were first analyzed and compared. A modified scheme based on the two kinds of models was proposed. Moreover, a modified current statistical model based-fuzzy adaptive extended Kalman filter (MCS-FAEKF) algorithm was proposed to choose maneuvering model and adjust system noise covariance dynamically. The simulated results show that the algorithm could get more accurate and reliable performance for vehicle positioning compared with the current statistical model based-extended Kalman filter (CS-EKF) and Singer-EKF algo-rithms.关键词
Singer模型/“当前”统计模型/车辆定位/模糊自适应/扩展卡尔曼滤波Key words
Singer model/current statistical model/vehicle positioning/fuzzy adaptive/extended Kalman filter分类
信息技术与安全科学引用本文复制引用
邵震洪,李文峰,吴怡,杨琼,沈连丰..基于改进当前统计模型的模糊自适应车辆定位算法[J].通信学报,2013,(7):184-190,7.基金项目
国家高技术研究发展计划(“863”计划)基金资助项目(2008AA01Z205)，国家自然科学基金资助项目(61171081)；江苏省技术创新基金资助项目(BC2012006)；教育部新世纪优秀人才支持计划基金资助项目(NCET-10-0018)Foundation Items：The National High Technology Research and Development Program of China (863 Program)(2008AA01Z205) （“863”计划）
The National Natural Science Foundation of China (61171081) （61171081）
The Innovation Technology Found of Jiangsu Province (BC2012006) （BC2012006）
The Program of New Century Excellent Talents in University of China (NCET-10-0018) （NCET-10-0018）
