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.