Reduced-order adaptive Kalman filtering for dual-frequency navigation with carrier phase

Abstract

A new adaptive Kalman filtering algorithm for dual-frequency navigation with carrier phase is presented. By reducing the filtering order after full-order initialization, this algorithm saves the extra computational cost brought by carrier phase observations. The improved adaptation of state covariance matrix in reduced-order processing also improves filtering precision and robustness. Finally applications on both static data and kinetic simulations demonstrate the validity and efficiency of the algorithm.