Next Article in Journal
Passive Micromixers with Interlocking Semi-Circle and Omega-Shaped Modules: Experiments and Simulations
Next Article in Special Issue
Biomimetic-Based Output Feedback for Attitude Stabilization of Rigid Bodies: Real-Time Experimentation on a Quadrotor
Previous Article in Journal
A Concentration-Controllable Microfluidic Droplet Mixer for Mercury Ion Detection
Previous Article in Special Issue
PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation
Article Menu

Export Article

Open AccessArticle
Micromachines 2015, 6(7), 926-952; doi:10.3390/mi6070926

A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems

1
Mobile Multi-Sensor Systems (MMSS) Research Group, Department of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
2
College of Automation, Harbin Engineering University, Harbin 150001, China
3
GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editors: Aboelmagd Noureldin and Stefano Mariani
Received: 31 May 2015 / Revised: 8 July 2015 / Accepted: 9 July 2015 / Published: 16 July 2015
(This article belongs to the Special Issue Next Generation MEMS-Based Navigation—Systems and Applications)
View Full-Text   |   Download PDF [2153 KB, uploaded 16 July 2015]   |  

Abstract

Numerous solutions/methods to solve the existing problems of pedestrian navigation/localization have been proposed in the last decade by both industrial and academic researchers. However, to date there are still major challenges for a single pedestrian navigation system (PNS) to operate continuously, robustly, and seamlessly in all indoor and outdoor environments. In this paper, a novel method for pedestrian navigation approach to fuse the information from two separate PNSs is proposed. When both systems are used at the same time by a specific user, a nonlinear inequality constraint between the two systems’ navigation estimates always exists. Through exploring this constraint information, a novel filtering technique named Kalman filter with state constraint is used to diminish the positioning errors of both systems. The proposed method was tested by fusing the navigation information from two different PNSs, one is the foot-mounted inertial navigation system (INS) mechanization-based system, the other PNS is a navigation device that is mounted on the user’s upper body, and adopting the pedestrian dead reckoning (PDR) mechanization for navigation update. Monte Carlo simulations and real field experiments show that the proposed method for the integration of multiple PNSs could improve each PNS’ navigation performance. View Full-Text
Keywords: pedestrian navigation system (PNS); state constraint; Kalman filter; inertial navigation system (INS); pedestrian dead reckoning (PDR) pedestrian navigation system (PNS); state constraint; Kalman filter; inertial navigation system (INS); pedestrian dead reckoning (PDR)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Lan, H.; Yu, C.; Zhuang, Y.; Li, Y.; El-Sheimy, N. A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems. Micromachines 2015, 6, 926-952.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Micromachines EISSN 2072-666X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top