Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System

Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this f...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 15; no. 5; pp. 10872 - 10890
Main Authors: Yuan, Xuebing, Yu, Shuai, Zhang, Shengzhi, Wang, Guoping, Liu, Sheng
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 07.05.2015
MDPI
Subjects:
Accuracy
Algorithms
Gyroscopes
heading estimation
Indoor
indoor navigation
Inertial navigation
Kalman filters
Localization
Magnetometers
multi-sensor
Power
quaternion
Radio frequency identification
Rotorcraft
Sensors
Unmanned aerial vehicles
unscented Kalman filter
Wearable
China
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Inertial navigation based on micro-electromechanical system (MEMS) inertial measurement units (IMUs) has attracted numerous researchers due to its high reliability and independence. The heading estimation, as one of the most important parts of inertial navigation, has been a research focus in this field. Heading estimation using magnetometers is perturbed by magnetic disturbances, such as indoor concrete structures and electronic equipment. The MEMS gyroscope is also used for heading estimation. However, the accuracy of gyroscope is unreliable with time. In this paper, a wearable multi-sensor system has been designed to obtain the high-accuracy indoor heading estimation, according to a quaternion-based unscented Kalman filter (UKF) algorithm. The proposed multi-sensor system including one three-axis accelerometer, three single-axis gyroscopes, one three-axis magnetometer and one microprocessor minimizes the size and cost. The wearable multi-sensor system was fixed on waist of pedestrian and the quadrotor unmanned aerial vehicle (UAV) for heading estimation experiments in our college building. The results show that the mean heading estimation errors are less 10° and 5° to multi-sensor system fixed on waist of pedestrian and the quadrotor UAV, respectively, compared to the reference path.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s150510872