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Improving Real-Time Position Estimation Using Correlated Noise Models
Andrew Martin1, Matthew Parry2, Andy W R Soundy1
1Department of Physics, University of Otago, 730 Cumberland St, Dunedin 9016, New Zealand.
Sensors (Basel, Switzerland)
|October 23, 2020
Summary
We developed new algorithms for real-time Global Positioning System (GPS) location estimation and uncertainty quantification. The best method uses an Ornstein-Uhlenbeck noise model with an enhanced Kalman Filter, outperforming standard approaches.
Area of Science:
- Geomatics Engineering
- Signal Processing
- Navigation Systems
Background:
- Accurate real-time Global Positioning System (GPS) location inference and uncertainty quantification are critical for navigation.
- Existing Kalman Filter (KF) approaches often assume Gaussian noise, which can lead to underestimation of position uncertainties.
Purpose of the Study:
- To develop and evaluate novel algorithms for real-time GPS location estimation and uncertainty quantification.
- To compare the performance of different algorithms, including those with enhanced noise models.
Main Methods:
- Algorithms were tested using GPS data from the Southern Hemisphere at various latitudes.
- The Ornstein-Uhlenbeck (OU) noise model was integrated into an enhanced Kalman Filter (KF).
- Performance was ranked using the log-score rule; dilution-of-precision parameters were also evaluated.
Main Results:
- The enhanced KF with the OU noise model demonstrated superior performance in capturing autocorrelated process noise.
- This approach significantly outperformed a standard KF with a Gaussian noise model.
- GPS dilution-of-precision parameters provided minimal benefit for uncertainty quantification.
Conclusions:
- The enhanced KF with an OU noise model offers accurate real-time GPS positioning and uncertainty quantification.
- The method is computationally suitable for embedded systems and sensor fusion applications.
- It provides a robust foundation for integrating data from complementary sensors like accelerometers and magnetometers.
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