基于改进当前统计模型的模糊自适应车辆定位算法

doi:10.3969/j.issn.1000-436x.2013.07.021

Journal on Communications ›› 2013, Vol. 34 ›› Issue (7): 184-190.doi: 10.3969/j.issn.1000-436x.2013.07.021

• Academic communication • Previous Articles

Fuzzy adaptive algorithm based on modified current statistical model for vehicle positioning

Zhen-hong SHAO^1,²,Wen-feng3 LI³,Yi WU¹,Qiong YANG¹,Lian-feng SHEN¹

¹ National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China;
² Nanjing Telecommunication Technology Institute, Nanjing 210007, China;
³ School of Electronic Science and Engineering, Nanji g University, Nanjing 210046, China

Online:2013-07-25 Published:2017-06-24
Supported by:
The National High Technology Research and Development Program of China (863 (;The National Natural Science Foundation of China;The Innovation Technology Found of Jiangsu Province;The Program of New Century Excellent Talents in Univers ity of China

Abstract

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.

Key words: Singer model, current statistical model, vehicle positioning, fuzzy adaptive; extended Kalman filter

Zhen-hong SHAO,Wen-feng3 LI,Yi WU,Qiong YANG,Lian-feng SHEN. Fuzzy adaptive algorithm based on modified current statistical model for vehicle positioning[J]. Journal on Communications, 2013, 34(7): 184-190.

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References 14

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