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Advanced mobile apps employ MEMS sensors

Posted: 27 Jun 2011 ?? ?Print Version ?Bookmark and Share

Keywords:MEMS? sensors? mobile applications?

Recent advances in MEMS processes, MEMS ACC and GYRO have been continuously providing higher performance and nearer to the level of tactical-grade devices. In a short time period such as 1 minute, unaided ACC and GYRO can give relatively accurate position measurements. This is useful to form GPS/SINS integrated navigation systems when the GPS signal is blocked.

Usually for consumer electronics five percent of error on distance travelled is acceptable for indoor PDR. For example, when the pedestrian walks 100m, the error should be within 5m. This requires the heading error to be within 2 to 5 [2]. For instance, when heading error is 2, then the position error for 100m traveled distance will be 3.5m [= 2*100m*sin (2/2)].

In addition, MEMS pressure sensor is able to measure absolute air pressure with respect to sea level. Therefore, the altitude of mobile user from 600m below sea level to 9000m above sea level can be determined to aid GPS height measurement [2]. Figure 2 shows the PDR block diagram for MEMS sensors and GPS.

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Figure 2: PDR block diagram in a mobile device.

MEMS sensor fusion
Sensor fusion is a set of digital filtering algorithms to compensate the disadvantages of each individual sensor and then output accurate dynamic attitude information pitch/roll/heading. The purpose of sensor fusion is to take each sensor measurement data as input and then apply digital filtering algorithms to compensate each other and output accurate and responsive dynamic attitude results. Therefore, the heading or orientation is immune to environmental magnetic disturbance as to the bias drift of the gyroscope.

Tilt compensated E-Compass, which consists of a 3-axis ACC and a 3-axis MAG, can provide heading with respect to earth magnetic north. But this heading is sensitive to environmental magnetic disturbance. With the installation of a 3-axis GYRO, it is possible to develop 9-axis sensor fusion solution to maintain accurate heading anywhere and anytime.

When designing a system using ACC, GYRO, MAG and PS, it is important to understand the advantages and disadvantages of each MEMS sensor.

ACC: It can be used for tilt compensated digital compass in static or slow motion and it can be used for pedometer step counter and to detect if the system is in motion or at rest. However, an ACC cannot differentiate the true linear acceleration from earth gravity components when the system is at motion in 3D space and it is sensitive to shake and vibration.

GYRO: It can continuously provide rotation matrix from system body coordinates to local earth horizontal coordinates and it can aid the digital compass for heading calculations when the MAG gets disturbed. But the bias drift over time leads to unlimited attitude and position error.

MAG: It can calculate absolute heading with respect to earth magnetic north and can be used to calibrate the gyroscope sensitivity but it is sensitive to environmental magnetic interference fields.

PS: It can be used to tell which floor you are on for indoor navigation and aid GPS for altitude calculation and positioning accuracy when GPS signal is degraded but it is sensitive to wind flow and weather conditions.

Due to the above considerations, the Kalman filter appears today as the most common mathematical instrument to fuse the information coming from the different sensors. It weights the different sensors contribution most heavily where they have the best performances, thus providing more accurate and stable estimates than a system based on any one medium alone [3].

Currently, quaternion based extended Kalman filter (EKF) is a popular scheme for sensor fusion because quaternion has only 4 elements compared to rotation matrix with 9 elements and it can also avoid the singularity issue that is present in the rotation matrix [3].

Conclusion
The main challenge for advanced mobile applications, such as the AR, is accurate position and orientation anywhere and anytime because the AR is closely related to the PDR or the LBS. With the limitation of GPS receiver, MEMS sensors are an attractive solution for indoor PDR since most of these sensors are already available in smart phones.

In order to achieve the allowable five percent indoor PDR position error, MEMS sensor fusion algorithms need to be developed to compensate the disadvantages of each sensor. As the performance of MEMS sensors is continuously improving, the user-independent SINS/GPS integrated navigation system will be common in smart phones in the near future.

- Jay Esfandyari, Paolo Bendiscioli and Gang Xu
??STMicroelectronics

References
1. A. Lawrence, Modern Inertial Technology: Navigation, Guidance, and Control, ISBN: 978-0387985077 (hardback), 0387985077 (electronic), 1998

2. STMicroelectronics, Inc.

J. Esfandyari et al, MEMS Pressure Sensors in Pedestrian Navigation, Sensors Magazine, Dec. 2010
http://www.sensorsmag.com/electronics-computers/consumer/mems-pressure-sensors-pedestrian-navigation-7896

3. Greg Welch, Gary Bishop, An Introduction to the Kalman Filter, University of North Carolina at Chapel Hill

4. A. Sabatini, Quaternion-Based Extended Kalman Filter for Determining Orientation by Inertial and Magnetic Sensing, IEEE transaction on biomedical engineering, Vol. 53, No. 7, July 2006
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1643403

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