A New GPS/RFID Integration Algorithm Based on Iterated Reduced Sigma Point Kalman Filter for Vehicle Navigation

被引：0
|
作者：
Peng, Jing [1 ,2 ]
Wu, Falin [3 ]
Zhu, Ming [1 ]
Zhang, Kefei [1 ]
Wang, Feixue [2 ]
机构：
[1] RMIT Univ, Sch Math & Geospatial Sci, Melbourne, Vic, Australia
[2] Natl Univ Def Technol, Sch Elect Sci & Engn, Changsha, Hunan, Peoples R China
[3] Beijing Univ Aeronaut & Astronaut, Sch Instrument & Opto Elect Engn, Beijing, Peoples R China
来源：
PROCEEDINGS OF THE 22ND INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS 2009) | 2009年
关键词：
D O I：
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中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
To improve the accuracy, reliability and availability of GPS navigation service in urban area, a new GPS/RFID integration method for vehicle navigation is proposed in this paper. In the proposed method, a RFID system is used to aid GPS to achieve a high accuracy positioning via the Received Signal Strength (RSS) measurements and sparse location information of RFID tags. An iterated Reduced Sigma Point Kalman Filter is proposed as a predominant filter for the GPS/RFID integration as well. The results of field experiment show that both accuracy and availability of positioning can be improved by this low-cost GPS/RFID integration method significantly.
引用
收藏
页码：803 / 810
页数：8
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