Multi-sensor Attitude and Heading Reference System using Genetically Optimized Kalman Filter

An Attitude and Heading Reference System (AHRS) comprising accelerometers, gyroscopes and magnetometers can provide roll, pitch and heading information. AHRS is utilized in many applications such as navigation, augmented/virtual reality, and mobile mapping. The AHRS mechanization involves integration of angular rate measurement to provide high rate orientation but with unbounded drifts due to accumulation of random noise. To reduce drifts, mechanization output is combined with absolute measurement from magnetometer and accelerometer using Extended Kalman Filter(EKF). EKF accuracy is greatly affected by process covariance matrix (Q) and measurement noise covariance matrix(R). Conventional stochastic modeling approaches to determine Q and R parameters do not guarantee best performance. This paper proposes a systematic procedure for EKF parameters optimization using a hybrid statistical and genetic algorithms (GA) approach. The proposed approach has been verified on real data collected by an inertial measurement unit. Results showed that the Q and R can be optimized within few GA iterations outperforming conventional EKF parameter estimation methods.