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.

循环滑移的处理是基于载波相位的高精度定位的关键技术之一。对于在不精确的杠杆臂条件下基于伪距和多普勒测量的基于紧密耦合积分系统的单站惯性辅助循环滑移检测性能较差的问题，提出了一种基于ARIMA-GARCH（自回归积分移动平均值）的改进实时方法提出了广义广义自回归条件异方差模型。ARIMA（1，1，0）模型用于使基本检验统计量下降，然后，GARCH（1,1）模型用于估计具有波动性聚类特征的残差的条件方差。考虑到实时应用的需求，两种算法的模型参数分别在每个时期和周期性变化，被提议。基于从车辆和飞行测试收集的真实数据进行了两种数值实验。结果表明，该方法不仅可以大大降低误报率，而且可以检测北斗系统所有卫星全频率的模拟1周期滑移，并且由于其良好的性能而推荐使用周期变化算法。在线检测周转率低且计算成本低。