A robust Kalman filter time scale algorithm with data anomaly

Abstract

Kalman filtering is utilized in many fields because of its capability to separate data from white phase noise. In time and frequency domain, employing Kalman filter is particularly important because of its use in building time scales. The studied time scale algorithms have been usually based on an ensemble of clocks without data anomaly, or the anomaly data is processed in advance to secure the reliability of the data used in Kalman filter algorithm. This increases the amount of computation and affects the real-time performance of the algorithm. In this study a robust Kalman filter is employed to present a method of time scale calculation. It extends a previously published Kalman filter algorithm that is useful for an ensemble of clocks without phase anomalies. In the algorithm, the inflation factor and the optimal adaptive factor are applied to the clock ensemble. The introduced algorithm may be useful for an ensemble of clocks with measurement outliers and phase jumps. The effectiveness of the proposed method can be verified through simulation and experimental analysis. The analysis result shows that the robust Kalman filter algorithm can resist the influence of measurement outliers and phase jumps on time scale performance. So, the accuracy and the stability of time scale can be improved.