Filtering Analysis of Navigation Data Processing for Personnel Positioning System
Science Journal of Applied Mathematics and Statistics
Volume 4, Issue 3, June 2016, Pages: 97-100
Received: Apr. 18, 2016; Accepted: Apr. 28, 2016; Published: May 13, 2016
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Authors
Lianhong Ding, School of Information, Beijing Wuzi University, Beijing, China
Hongqing Sang, School of Logistics Engineering, School of Information, Beijing Wuzi University, Beijing, China
Juntao Li, School of Information, Beijing Wuzi University, Beijing, China
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Abstract
Ultra wideband technology is a more precise indoor positioning technology. But the UWB positioning output would be unstable if the signal from base station were blocked. The low cost inertial positioning is a method to make up a method for indoor navigation. However, the positioning error will accumulate quickly due to the low cost inertial measurement error. To solve this problem, we selected the MPU6050 module as a chip and Simulated test with Extended Kalman Filter and Unscented Kalman Filter algorithms, and carried out the error analysis on both of them. Finally, come to sampling Kalman filter estimation accuracy estimation is more accurate, more suitable for MPU6050 positioning algorithm.
Keywords
Indoor Positioning, Location Algorithm, Combined Positioning, Extended Calman Filter
To cite this article
Lianhong Ding, Hongqing Sang, Juntao Li, Filtering Analysis of Navigation Data Processing for Personnel Positioning System, Science Journal of Applied Mathematics and Statistics. Vol. 4, No. 3, 2016, pp. 97-100. doi: 10.11648/j.sjams.20160403.12
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Wang Jianping, Xu Heng, Li Qiyue. The mobile node localization Calman filtering algorithm on [J]. Journal of electronic measurement and instrument based on the 2013, 02: 120-126.
[2]
Xia Nan, Qiu Tianshuang, Li Jingchun, Li Shufang. A nonlinear filtering algorithm based on Calman filter and particle filter [J]. Journal of electronics, 2013, 01: 148-152.
[3]
Gao Mingyu. He Zhiwei, Xu Jie. Based on sampling points Kalman filtering power battery SOC estimation [J]. Electrotechnical Journal of, 2011, 11: 161-167.
[4]
Zhang Qiuzhao, Zhang Shubi, Zheng Nanshan, Wang Jian. The multiple fading robust volume of the combined system of Calman GPS/INS filter [J]. Journal of China University of Mining and Technology, 2014, 01: 162-168.
[5]
Repair spring wave, Ren Xiao, Li Yanqing, Liu Mingfeng. Short term prediction method of wind speed series based on Calman filter [J]. Journal of electrical engineering, 2014, 02: 253-259.
[6]
Liu Qi, Yan Li, Zhou. The technical characteristics and development direction of modern telecommunication technology [J]. UWB, 2009, 10: 6-10+18.
[7]
Wang Hongjian, Jing Wong, Bian Xinqian, Fu Guixia. SLAM [J]. EKF under water robot combined navigation based on 2012, 01: 56-64.
[8]
Wang Lu, Li Guangchun, Qiao Xiangwei, Wang Zhaolong, Ma Tao. Adaptive UKF algorithm [J]. automation of the maximum likelihood criterion and expectation maximization algorithm based on 2012, 07: 1200-1210.
[9]
Lin Zhao, Wang Xiaoxu, Sun Ming, Ding Jicheng, Yan Chao. [J]. automation of adaptive UKF filtering algorithm for maximum a posteriori estimation and weighted index based on 2010, 07: 1007-1019.
[10]
Ge Quanbo, Li Wenbin, Sun Ruoyu, Xu Zi. Estimation of [J]. automation of EKF centralized fusion based on 2013,06:816-825.
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