Design and Performance of Wheel-mounted MEMS IMU for Vehicular Navigation

In modern cars MEMS gyroscopes and accelerometers provide essential measurements for enhancing the stability and control. Both types of sensors have significant noise at low frequencies, limiting the measurement accuracy especially in low dynamic conditions. In addition, uncompensated accelerometer tilt causes large bias to acceleration estimates. For gyroscopes, physical rotation of the sensor can be used to remove the constant part of the gyro errors and reduce low-frequency noise. In ground vehicles such rotation exists conveniently in wheels. When inertial sensors are attached to wheel, both types of sensors provide information on the rotation, gyroscopes naturally and accelerometers via specific force measurement. In addition, as a result of carouseling, accurate wheel heading, roll and pitch estimation can be estimated with high resolution, and the result is nearly bias-free. Combining the wheel orientation to distance traveled via known radius enables classic dead reckoning mechanization (assuming zero slip) and other vehicle dynamics monitoring systems (considering wheel slip as unknown to be solved). In the paper, we provide details of wheel-mounted inertial system hardware and algorithms and show test results for several system configurations and applications. We discuss future system improvements, in particular, system miniaturization and an energy-harvesting development progress for next-generation inertial systems.

[1]  W.J. Fleming,et al.  New Automotive Sensors—A Review , 2008, IEEE Sensors Journal.

[2]  Aboelmagd Noureldin,et al.  Modeling the Stochastic Drift of a MEMS-Based Gyroscope in Gyro/Odometer/GPS Integrated Navigation , 2010, IEEE Transactions on Intelligent Transportation Systems.

[3]  Benjamin Matthew Renkoski The effect of carouseling on MEMS IMU performance for gyrocompassing applications , 2008 .

[4]  Ahmed El-Rabbany,et al.  Temperature variation effects on stochastic characteristics for low-cost MEMS-based inertial sensor error , 2007 .

[5]  Terry Moore,et al.  Investigating the use of Rotating Foot Mounted Inertial Sensors for Positioning , 2012 .

[6]  Aboelmagd Noureldin,et al.  Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications , 2009, IEEE Transactions on Vehicular Technology.

[7]  Wei Sun,et al.  Accuracy improvement of SINS based on IMU rotational motion , 2012, IEEE Aerospace and Electronic Systems Magazine.

[8]  J. Takala,et al.  Bias Prediction for MEMS Gyroscopes , 2012, IEEE Sensors Journal.

[9]  Edward S. Geller,et al.  Inertial System Platform Rotation , 1968, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Yong Yang,et al.  Fiber-optic strapdown inertial system with sensing cluster continuous rotation , 2004 .

[11]  S. Jairam,et al.  Verification of a MEMS based adaptive cruise control system using simulation and semi-formal approaches , 2008, 2008 15th IEEE International Conference on Electronics, Circuits and Systems.

[12]  Isaac Skog,et al.  Performance characterisation of foot-mounted ZUPT-aided INSs and other related systems , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[13]  Feng Sun,et al.  Researching on the compensation technology of rotating mechanism error in single-axis rotation strapdown inertial navigation system , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[14]  T.E. Melgard,et al.  GPS signal availability in an urban area-receiver performance analysis , 1994, Proceedings of 1994 IEEE Position, Location and Navigation Symposium - PLANS'94.

[15]  Jussi Collin,et al.  MEMS IMU Carouseling for Ground Vehicles , 2015, IEEE Transactions on Vehicular Technology.

[16]  P. Savage Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms , 1998 .

[17]  Daniel N. Aloi,et al.  Comparative Performance Analysis of a Kalman Filter and a Modified Double Exponential Filter for GPS-Only Position Estimation of Automotive Platforms in an Urban-Canyon Environment , 2007, IEEE Transactions on Vehicular Technology.

[18]  Martti Kirkko-Jaakkola,et al.  Indoor Localization Methods Using Dead Reckoning and 3D Map Matching , 2014, J. Signal Process. Syst..

[19]  Bradford W. Parkinson,et al.  Cascaded Kalman Filters for Accurate Estimation of Multiple Biases, Dead-Reckoning Navigation, and Full State Feedback Control of Ground Vehicles , 2007, IEEE Transactions on Control Systems Technology.

[20]  David M. Bevly,et al.  GNSS for Vehicle Control , 2010 .

[21]  Hervé Mathias,et al.  A low drift, low noise detection IC applied to MEMS gyros , 2007, 2007 14th IEEE International Conference on Electronics, Circuits and Systems.

[22]  Jarmo Takala,et al.  Effect of Carouseling on Angular Rate Sensor Error Processes , 2015, IEEE Transactions on Instrumentation and Measurement.

[23]  Xiaoying Gao,et al.  Research on accuracy improvement of INS with continuous rotation , 2009, 2009 International Conference on Information and Automation.

[24]  Sagi Filin,et al.  Analytical Observability Analysis of INS with Vehicle Constraints , 2015 .

[25]  Elecia White,et al.  Fusion Filter Algorithm Enhancements For a MEMS GPS/IMU , 2001 .