Real-time attitude tracking of mobile devices

This paper provides a real-time attitude determination algorithm using gyros, accelerometers, and magnetometers on consumer portable devices. The main advantage of this algorithm is that uses a Kalman filter algorithm and utilizes multi-level constraints, including pseudo-observation updates, measurements from accelerometers and magnetometers, and the quasi-static attitude updates. Walking tests with different brands of smartphones showed that the algorithm provided promising absolute heading results outdoors, and provided smooth relative heading results indoors with different phone places such as handheld, at an ear, dangling with hand, and in a pants pocket.

[1]  Valérie Renaudin,et al.  Magnetic, Acceleration Fields and Gyroscope Quaternion (MAGYQ)-Based Attitude Estimation with Smartphone Sensors for Indoor Pedestrian Navigation , 2014, Sensors.

[2]  Xiaoji Niu,et al.  Autonomous Calibration of MEMS Gyros in Consumer Portable Devices , 2015, IEEE Sensors Journal.

[3]  Sébastien Ourselin,et al.  In Use Parameter Estimation of Inertial Sensors by Detecting Multilevel Quasi-static States , 2005, KES.

[4]  Yanhua Zhang,et al.  Adaptive filter for a miniature MEMS based attitude and heading reference system , 2004, PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556).

[5]  Chuan Heng Foh,et al.  A practical path loss model for indoor WiFi positioning enhancement , 2007, 2007 6th International Conference on Information, Communications & Signal Processing.

[6]  Jianhu Zhao,et al.  Study on underwater navigation system based on geomagnetic match technique , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[7]  Philippe Martin,et al.  Design and implementation of a low-cost observer-based attitude and heading reference system , 2010 .

[8]  Valérie Renaudin,et al.  Design and Testing of a Multi-Sensor Pedestrian Location and Navigation Platform , 2012, Sensors.

[9]  Quan Zhang,et al.  An in situ hand calibration method using a pseudo-observation scheme for low-end inertial measurement units , 2012 .

[10]  Sara Saeedi,et al.  Context-Aware Personal Navigation Services Using Multi-level Sensor Fusion Algorithms , 2013 .

[11]  Xiaoji Niu,et al.  Fast Thermal Calibration of Low-Grade Inertial Sensors and Inertial Measurement Units , 2013, Sensors.

[12]  Quentin Ladetto,et al.  Combining Gyroscopes, Magnetic Compass and GPS for Pedestrian Navigation , 2001 .

[13]  Guanrong Chen,et al.  Introduction to random signals and applied Kalman filtering, 2nd edn. Robert Grover Brown and Patrick Y. C. Hwang, Wiley, New York, 1992. ISBN 0‐471‐52573‐1, 512 pp., $62.95. , 1992 .

[14]  Tom Chau,et al.  A Review of Indoor Localization Technologies: towards Navigational Assistance for Topographical Disorientation , 2010 .

[15]  Jinling Wang,et al.  A Novel Method to Integrate IMU and Magnetometers in Attitude and Heading Reference Systems , 2011, Journal of Navigation.

[16]  M. J. Caruso,et al.  Applications of magnetic sensors for low cost compass systems , 2000, IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062).

[17]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .

[18]  Valérie Renaudin,et al.  Use of Earth’s Magnetic Field for Mitigating Gyroscope Errors Regardless of Magnetic Perturbation , 2011, Sensors.

[19]  Hassen Fourati,et al.  Heterogeneous Data Fusion Algorithm for Pedestrian Navigation via Foot-Mounted Inertial Measurement Unit and Complementary Filter , 2015, IEEE Transactions on Instrumentation and Measurement.

[20]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[21]  William F Storms,et al.  Magnetic Field Aided Indoor Navigation , 2012 .