A real-time inertial-aided cycle slip detection method based on ARIMA-GARCH model for inaccurate lever arm conditions

Abstract

The processing of cycle slip is one of the key techniques of carrier-phase-based high-precision positioning. As for the poor performance of the single-station inertial-aided cycle slip detection based on the tightly coupled integration system using pseudorange and Doppler measurements under inaccurate lever arm conditions, an improved real-time method based on ARIMA-GARCH (autoregressive integrated moving average-generalized autoregressive conditional heteroscedastic) model is proposed. The ARIMA (1, 1, 0) model is used to detrend the fundamental test statistics, and then, the GARCH (1,1) model is used to estimate the conditional variance of the residuals with the characteristics of volatility clustering. Considering the needs for real-time applications, two algorithms with model parameters varied at every epoch and periodically, respectively, are proposed. Two kinds of numerical experiments based on the real data collected from vehicular and flight tests are carried out. The results show that the proposed method can not only greatly reduce the false alarm rate but also can detect simulated 1-cycle slips for all frequencies of all satellites of BDS (BeiDou System) and the period-varied algorithm is recommended due to its good performance of cycle slip detection and low computation cost online.