Estimation on IMU Yaw Misalignment by Fusing Information of Automotive Onboard Sensors

In the development of automotive electronics, nearly every automobile is equipped with an inertial measurement unit (IMU). However, the yaw misalignment of the IMU is inevitable when mounted to a vehicle's body. It is difficult to measure directly, so its estimation is required to acquire accurate data from the IMU. This study proposes a method for the IMU and automotive onboard sensors to estimate the yaw misalignment autonomously. In order to estimate the IMU yaw misalignment, first, the attitude and velocity integration method in the vehicle level frame is presented. In addition, the attitude error dynamics consisting of yaw misalignment, pitch, and roll, and velocity error dynamics consisting of longitudinal and lateral velocities, are derived. Then, on the basis of the error dynamics and observation equations, the degree of the observability of the yaw misalignment is analyzed through the piece-wise constant system (PWCS) and singular value decomposition (SVD) theory. Next, a Kalman filter is implemented to estimate the yaw misalignment and the velocity error. Finally, an experimental test in straight line acceleration and deceleration maneuvers is conducted to verify the yaw misalignment estimation method. When the longitudinal or lateral acceleration varies, the yaw misalignment is observable and can be estimated without aids from external information. After compensating for the yaw misalignment, the accuracy of the state estimation result, such as lateral velocity, can be improved significantly when the IMU is integrated with other sensors.