全球导航卫星系统应用
计算机科学
自回归积分移动平均
全球定位系统
离群值
卫星系统
数据挖掘
时间序列
实时计算
人工智能
机器学习
电信
作者
Jingwen Guo,Yilan Zhou,Shuai Zhao,Zhijian Hu
标识
DOI:10.1088/1361-6501/ace19b
摘要
Abstract 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.
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