Hybrid Ranging UWB Precise Positioning Model and XGBoost Classification Algorithm

In recent years, the use of Ultra Wide Band sub nanosecond ultra narrow pulse for short-range accurate positioning has become a research hotspot. This paper uses the data set of the 18th Chinese graduate mathematical modeling competition to study. Firstly, this paper removes some outliers with obvious anomalies, and uses multi-scale wavelet transform and Kalman filter algorithm to denoise the data. Secondly, this paper constructs the Time of Arrival positioning model based on Newton iterative method and the improved Time Difference of Arrival positioning model to locate the data without signal interference. It is found that the latter has higher positioning accuracy. In order to deal with abnormal data with signal interference, the three-dimensional positioning model of least square method considering ranging error is constructed in this paper. Finally, in order to distinguish whether the data source has signal interference or not, XGBoost decision tree algorithm is used to classify the data.