A new GNSS outlier mitigation method for GNSS/INS integrated system

High-precision positioning with global navigation satellite systems (GNSS) remains a significant challenge in urban environments, due to the outliers caused by the insufficient number of accessible satellites and environmental interference. A GNSS outlier mitigation algorithm with effective fault detection and exclusion (FDE) is required for high-precision positioning. The traditional methods are designed to deal with zero-mean noise in GNSS, which leads to instabilities under biased measurements. Considering that GNSS data are typical time series data, a dynamic FDE scheme is constructed by combining a prediction-model-based method and a dissimilarity-based method. First, a hybrid prediction model which combines autoregressive integrated moving average (ARIMA) model and multilayer perceptron (MLP) model is proposed to provide pseudo-GNSS series by predicting the vehicle's location for several future steps. Then, a dissimilarity-based method of dynamic time warping measure is utilized to analyze the pairwise dis-similarity between the pseudo-GNSS series and the received GNSS series. The performance of the different models in forecasting is evaluated, and the results show that the positioning accuracy is significantly improved by applying the ARIMA-MLP. The effectiveness of the proposed FDE method is verified through simulation experiments and real experiments based on a typical urban canyon public dataset collected in Tokyo.

Automated summary

High-precision positioning in urban environments with GNSS is challenging due to outliers caused by limited satellites and environmental interference. To achieve high-precision positioning, an algorithm with effective fault detection and exclusion (FDE) for GNSS outliers is necessary. This study proposes a dynamic FDE scheme that combines a prediction-model-based method and a dissimilarity-based method to handle time series GNSS data. The results show that the proposed ARIMA-MLP model significantly improves positioning accuracy. Simulation and real experiments based on a Tokyo urban dataset validate the effectiveness of the FDE method.