A detection method of mismatched measures in GNSS coordinate time series: Fuzzy logic and IQR (Interquartile Range) based approach (original) (raw)

2023, A detection method of mismatched measures in GNSS coordinate time series: Fuzzy logic and IQR (Interquartile Range) based approach

This study presents a new approach for outlier detection aiming to enhance the accuracy and reliability of GNSS data. Unlike traditional approaches, this approach combines fuzzy logic and the Interquartile Range (IQR) method to improve outlier detection. The fuzzy logic-based method is employed to flexibly model data characteristics. Individual outlier scores are calculated for each data point using fuzzy logic, and these scores are then utilized to identify outliers in the dataset. By determining a threshold value based on the spread of the data around its center using the IQR method, data points scoring above this threshold can be considered outliers. The combination of these two methods ensures a more reliable and accurate outlier detection. When applying the proposed approach to a test signal containing obvious outlier values, it is observed that the processed time series exhibits a better normal distribution and improves performance metrics, indicating enhanced signal quality. Experimental results demonstrate the effectiveness of the proposed approach in effectively detecting outliers in GNSS coordinate time series. Overall, the proposed approach offers a promising solution for outlier detection in GNSS data by integrating fuzzy logic and the IQR method. It provides improved accuracy and reliability, leading to enhanced data analysis and interpretation in GNSS applications.