Implementation of a Low- Cost Multi- IMU by Using Information Form of a Steady State Kalman Filter

In this paper, a homogenous multi-sensor fusion method is used to estimate the true angular rate and acceleration with a combination of four low cost (< 10$) MEMS Inertial Measurement Units (IMU). An information form of steady state Kalman filter is designed to fuse the output of four low accuracy sensors to reduce the noise effect by the square root of the number of sensors. A hardware is implemented to test the method with three types of experiments: static test, constant rate, and oscillating test. Results of static test for z-axis show that ARW coefficient reduces to 0.0022°/√s and VRW error is decreased by %50. Also, dynamic test results show the reduction of the standard deviation of combined rate signal up to six times compared with a single sensor. A comparison between the proposed filter and the simple averaging method is made in which the results indicate that the Kalman filter is more accurate compared to the averaging method. Review History: Received: 16 October 2016 Accepted: 27 August 2017 Available Online: 17 September 2017

[1]  Liang Xue,et al.  Dynamic Performance Comparison of Two Kalman Filters for Rate Signal Direct Modeling and Differencing Modeling for Combining a MEMS Gyroscope Array to Improve Accuracy , 2015, Sensors.

[2]  Chi-Tsong Chen,et al.  Linear System Theory and Design , 1995 .

[3]  Isaac Skog,et al.  An open-source multi inertial measurement unit (MIMU) platform , 2014, 2014 International Symposium on Inertial Sensors and Systems (ISISS).

[4]  Wei Qin,et al.  An Integrated MEMS Gyroscope Array with Higher Accuracy Output , 2008, Sensors.

[5]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[6]  Richard J. Vaccaro,et al.  Statistical Modeling of Rate Gyros , 2012, IEEE Transactions on Instrumentation and Measurement.

[7]  Mark Newman,et al.  A New Approach to Better Low-Cost MEMS IMU Performance Using Sensor Arrays , 2013 .

[8]  Xiaoji Niu,et al.  Analysis and Modeling of Inertial Sensors Using Allan Variance , 2008, IEEE Transactions on Instrumentation and Measurement.

[9]  C. J. Harris,et al.  Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion , 2001 .

[10]  Isaac Skog,et al.  Inertial Sensor Arrays, Maximum Likelihood, and Cramér–Rao Bound , 2015, IEEE Transactions on Signal Processing.

[11]  D. W. Allan,et al.  Statistics of atomic frequency standards , 1966 .

[12]  M. Tanenhaus,et al.  Miniature IMU/INS with optimally fused low drift MEMS gyro and accelerometers for applications in GPS-denied environments , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[13]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[14]  Alireza Mohammad Shahri,et al.  Implementation of A low-cost multi-IMU hardware by using a homogenous multi-sensor fusion , 2016, 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA).

[15]  Liang Xue,et al.  Signal Processing of MEMS Gyroscope Arrays to Improve Accuracy Using a 1st Order Markov for Rate Signal Modeling , 2012, Sensors.

[16]  R. Bucy,et al.  Filtering for stochastic processes with applications to guidance , 1968 .

[17]  Liang Xue,et al.  Analysis of Dynamic Performance of a Kalman Filter for Combining Multiple MEMS Gyroscopes , 2014, Micromachines.

[18]  Liang Xue,et al.  Combining Numerous Uncorrelated MEMS Gyroscopes for Accuracy Improvement Based on an Optimal Kalman Filter , 2012, IEEE Transactions on Instrumentation and Measurement.