An IMU/magnetometer-based Indoor positioning system using Kalman filtering

Hellmers, Hendrik; Norrdine, Abdelmoumen; Blankenbach, Jörg; Eichhorn, Andreas

doi:10.1109/ipin.2013.6817887

Cited by 62 publications

(21 citation statements)

References 10 publications

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“…The inertial measurement unit (IMU) is used in this study. The accuracy of the inertial sensor units is significantly improved because of cutting edge microelectromechanical systems (MEMS) technology and sensor fusion with magnetometer [27], global positioning system (GPS) [28], or camera [29]. The IMU based on a combination of magnetometers and inertial sensors (accelerometer, gyroscope) can reduce drift effects of inertial sensors.…”

Section: B Previous Motion Capture Systemsmentioning

confidence: 99%

Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

Hwang

Reh²,

Effenberg³

et al. 2018

IEEE Trans. Consumer Electron.

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The background of our study is to apply advanced real-time gait analysis to walking interventions in daily-life setting. A vast of wearable devices provide gait information but not more than pedometer functions such as step counting, displacement and velocity. This paper suggests a real-time gait analysis method based on a head-worn inertial measurement unit (H-IMU). A novel analysis method implements real-time detection of gait events (heel strike, toe off, mid stance phase) and immediately provides detailed spatiotemporal parameters. The reliability of this method was proven by a measurement with over 11000 steps from seven participants on a 400 m outdoor track. The advanced gait analysis was conducted without any limitation of a fixed reference frame (e.g., indoor stage, infrared cameras). The mean absolute error in step-counting was 0.24%. Compared to a pedometer, additional gait parameters were obtained such as foot-ground contact time (CT) and contact time ratio (CTR). The gait monitoring system can be used as real-time and long-term feedback, which is applicable in the management of the health status and on injury prevention.

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Section: B Previous Motion Capture Systemsmentioning

confidence: 99%

Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

Hwang

Reh²,

Effenberg³

et al. 2018

IEEE Trans. Consumer Electron.

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“…EKF usually gives less accurate measure of covariance. It is incorporated in designing indoor positioning system based on IMU/magnetometer [23]. This filter also renders better and faster result when used in accelerometer sensor.…”

Section: Extended Kalman Filter (Ekf)mentioning

confidence: 99%

An Analysis on Position Estimation, Drifting and Accumulated Error Accuracy during 3D Tracking in Electronic Handheld Devices

Akhter¹,

Islam²,

Bhuiyan³

et al. 2018

JCC

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This work focuses on a brief discussion of new concepts of using smartphone sensors for 3D painting in virtual or augmented reality. Motivation of this research comes from the idea of using different types of sensors which exist in our smartphones such as accelerometer, gyroscope, magnetometer etc. to track the position for painting in virtual reality, like Google Tilt Brush, but cost effectively. Research studies till date on estimating position and localization and tracking have been thoroughly reviewed to find the appropriate algorithm which will provide accurate result with minimum drift error. Sensor fusion, Inertial Measurement Unit (IMU), MEMS inertial sensor, Kalman filter based global translational localization systems are studied. It is observed, prevailing approaches consist issues such as stability, random bias drift, noisy acceleration output, position estimation error, robustness or accuracy, cost effectiveness etc. Moreover, issues with motions that do not follow laws of physics, bandwidth, restrictive nature of assumptions, scale optimization for large space are noticed as well. Advantages of such smartphone sensor based position estimation approaches include, less memory demand, very fast operation, making them well suited for real time problems and embedded systems. Being independent of the size of the system, they can work effectively for high dimensional systems as well. Through study of these approaches it is observed, extended Kalman filter gives the highest accuracy with reduced requirement of excess hardware during tracking. It renders better and faster result when used in accelerometer sensor. With the aid of various software, error accuracy can be increased further as well.

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“…The KF algorithm, as a linear estimator, is used to fuse the data from gyros, accelerometers and magnetometers for pedestrian navigation [ 29 ]. Non-linear EKF [ 30 , 31 ] and PF [ 32 ] are applied in indoor positioning and autonomous navigation. In [ 10 , 33 ], two cascaded EKFs are employed to process the different device noises and then improve the final accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

An Accurate and Fault-Tolerant Target Positioning System for Buildings Using Laser Rangefinders and Low-Cost MEMS-Based MARG Sensors

Zhao

Guan

Landry

et al. 2015

Sensors

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Target positioning systems based on MEMS gyros and laser rangefinders (LRs) have extensive prospects due to their advantages of low cost, small size and easy realization. The target positioning accuracy is mainly determined by the LR’s attitude derived by the gyros. However, the attitude error is large due to the inherent noises from isolated MEMS gyros. In this paper, both accelerometer/magnetometer and LR attitude aiding systems are introduced to aid MEMS gyros. A no-reset Federated Kalman Filter (FKF) is employed, which consists of two local Kalman Filters (KF) and a Master Filter (MF). The local KFs are designed by using the Direction Cosine Matrix (DCM)-based dynamic equations and the measurements from the two aiding systems. The KFs can estimate the attitude simultaneously to limit the attitude errors resulting from the gyros. Then, the MF fuses the redundant attitude estimates to yield globally optimal estimates. Simulation and experimental results demonstrate that the FKF-based system can improve the target positioning accuracy effectively and allow for good fault-tolerant capability.

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An IMU/magnetometer-based Indoor positioning system using Kalman filtering

Cited by 62 publications

References 10 publications

Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

Real-Time Gait Analysis Using a Single Head-Worn Inertial Measurement Unit

An Analysis on Position Estimation, Drifting and Accumulated Error Accuracy during 3D Tracking in Electronic Handheld Devices

An Accurate and Fault-Tolerant Target Positioning System for Buildings Using Laser Rangefinders and Low-Cost MEMS-Based MARG Sensors

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