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Research on the Error of Global Positioning System Based on Time Series Analysis
Lijun Song1, Lei Zhou1, Peiyu Xu1
1School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.
Sensors (Basel, Switzerland)
|May 28, 2022
Summary
Global Positioning System (GPS) dynamic positioning accuracy is improved using Time Series Analysis and Kalman filtering. This method reduces errors in longitude and latitude, significantly enhancing positioning precision.
Area of Science:
- Geomatics Engineering
- Signal Processing
Background:
- Global Positioning System (GPS) exhibits poor dynamic positioning precision.
- Accurate positioning is crucial for various applications.
Purpose of the Study:
- To improve the dynamic positioning precision of GPS.
- To develop a method for eliminating random errors in GPS data.
Main Methods:
- Utilized Time Series Analysis (TSA) and Kalman filter technology.
- Constructed a GPS positioning error model using Autoregressive (AR) model based on Kalman filter.
- Applied the least square method for parameter estimation and adaptability tests.
Main Results:
- Reduced the maximum error, mean square error, and average absolute error in longitude and latitude.
- Demonstrated significant improvement in dynamic positioning precision after error correction.
Conclusions:
- The integrated approach effectively mitigates random errors in GPS dynamic positioning.
- The developed method offers a substantial enhancement in GPS positioning accuracy.
Keywords:
Autoregressive (AR) modelGlobal Positioning System (GPS)Kalman filteringTime Series Analysis (TAS)positioning precisionMore Related Videos
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