Kalman Filtering with Innovation Mean Method for INS/GPS Integrated Navigation Systems

Being a recursive technique which lends itself to implementation in a microcomputer, the Kalman filter is particularly suitable for on-line estimation. However, when measurement noise covariance R is much larger than process noise Q, the filtering effect will not be satisfied. A new algorithm, which we call innovation mean method, is proposed in this paper. The method makes different periods of measurement update and temporal update. In the measurement update period, innovation and its mean value is calculated. In the temporal update period, prediction update is implemented. Numerical simulation is implemented after the theory is induced. The algorithm is employed in integration navigation system based on global positioning systems (GPS) and inertial navigation system (INS). Simulation results show that the accuracy of innovation mean method is much better than that of basic Kalman filter.