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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MDPI AG
2015-05-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/15/5/10872 |
| _version_ | 1828391702132424704 |
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| author | Xuebing Yuan Shuai Yu Shengzhi Zhang Guoping Wang Sheng Liu |
| author_facet | Xuebing Yuan Shuai Yu Shengzhi Zhang Guoping Wang Sheng Liu |
| author_sort | Xuebing Yuan |
| collection | DOAJ |
| description | 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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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T07:06:47Z |
| publishDate | 2015-05-01 |
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| spelling | doaj.art-ea2a3910a0d143e68bc7103c05ce84a62022-12-22T01:58:11ZengMDPI AGSensors1424-82202015-05-01155108721089010.3390/s150510872s150510872Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor SystemXuebing Yuan0Shuai Yu1Shengzhi Zhang2Guoping Wang3Sheng Liu4School of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, ChinaSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, ChinaSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, ChinaSchool of Power and Mechanical Engineering, Wuhan University, 8 East Lake South Road, Wuhan 430072, ChinaSchool of Mechanical Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan 430074, ChinaInertial 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.http://www.mdpi.com/1424-8220/15/5/10872quaternionunscented Kalman filterheading estimationindoor navigationwearablemulti-sensor |
| spellingShingle | Xuebing Yuan Shuai Yu Shengzhi Zhang Guoping Wang Sheng Liu Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System Sensors quaternion unscented Kalman filter heading estimation indoor navigation wearable multi-sensor |
| title | Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System |
| title_full | Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System |
| title_fullStr | Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System |
| title_full_unstemmed | Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System |
| title_short | Quaternion-Based Unscented Kalman Filter for Accurate Indoor Heading Estimation Using Wearable Multi-Sensor System |
| title_sort | quaternion based unscented kalman filter for accurate indoor heading estimation using wearable multi sensor system |
| topic | quaternion unscented Kalman filter heading estimation indoor navigation wearable multi-sensor |
| url | http://www.mdpi.com/1424-8220/15/5/10872 |
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