Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter

被引:47
|
作者
Yu, Chunyang [1 ,2 ]
El-Sheimy, Naser [2 ]
Lan, Haiyu [2 ]
Liu, Zhenbo [2 ]
机构
[1] Haibin Engn Univ, Coll Automat, Harbin 150001, Heilongjiang, Peoples R China
[2] Univ Calgary, Dept Geomat Engn, Calgary, AB T2N 1N4, Canada
来源
MICROMACHINES | 2017年 / 8卷 / 07期
基金
加拿大自然科学与工程研究理事会;
关键词
map information; MEMS sensors; map aiding; map matching; auxiliary particle filter; cascade structure algorithm; indoor pedestrian navigation; MATCHING ALGORITHMS; INTEGRATION;
D O I
10.3390/mi8070225
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system's accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios.
引用
收藏
页数:16
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