Adaptive Kalman Filter for SINS/GPS Integration System with Measurement Noise Uncertainty

The Kalman filter has been extensively applied into the integrated strapdown inertial navigation system (SINS) and global positioning system (GPS). As the estimation of Kalman filter requires the correct measurement noise statistics, the uncertainty of GPS noise can degrade the accuracy of Kalman filter. In order to achieve the accurate navigation results with measurement noise uncertainty, an innovation covariance method-based adaptive Kalman filter is proposed. According to the innovation covariance method, a scale matrix is utilized to reduce the deviation of innovation covariance between its calculated value and the actual value. Next the filter gain is modified with this scale matrix. Moreover, a weighted innovation covariance estimator is proposed to obtain more accurate estimation of innovation covariance. The proposed adaptive Kalman filter is validated in the SINS/GPS system, and the estimation accuracy and navigation results of the SINS/GPS system with measurement noise uncertainty are greatly improved.