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Adaptive filtering based real-time position estimation with single-frequency navigation signals

A new real-time position estimation procedure using measurements from single-frequency navigation signals is presented in this paper. The new procedure possesses two features. One is that the wavelet reconstruction is taken advantage in the preprocessing to detect and correct cycle slips under strong noises of single-frequency combination signals. Another is that conventional adaptive Kalman filtering algorithm is improved by adjusting the state covariance matrix based on the previous estimators' update, and adjusting the measurement covariance matrix based on measurement innovations. Applications on both practical data and kinetic simulation prove the effectiveness of our new algorithms.