Wavelet Spectral Techniques for GPS Errors Reduction | ||
| The International Conference on Electrical Engineering | ||
| Article 111, Volume 7, 7th International Conference on Electrical Engineering ICEENG 2010, May 2010, Pages 1-8 PDF (763.82 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/iceeng.2010.33282 | ||
| Authors | ||
| Mohamed Elhabiby; Ahmed El-Ghazouly; Naser El-Sheimy | ||
| Mobile Multi-Sensor Systems (MMSS) Research Group, Department of Geomatics Engineering, the University of Calgary Calgary, Alberta, CANADA. | ||
| Abstract | ||
| ABSTRACT GPS measurements can be modeled as a true range plus other errors such as orbital and clock biases, atmospheric residual, multipath, and observation noise. Modeling is one approach to deal with some of these errors, if their characteristics are known (e.g. troposphere and ionosphere errors). Another way to deal with these errors is filtering in the frequency domain, where all these errors have different frequency spectrum component. Each errors is characterized by a specific frequency band, e.g. the receiver noise can be characterized with high frequency components, multipath errors, which have low to medium frequency bands, while the ionospheric and tropospheric errors are at a lower frequency band. Wavelet spectral techniques can separate GPS signal into sub-bands where different errors can be separated and mitigated. This paper introduces two new wavelet spectral analysis techniques to mitigate DGPS errors in the frequency domain namely, cycle slip and multipath errors. The first approach in this paper, Wavelet de-trending, is introduced to remove the long wavelength carrier phase multipath error in the measurement domain. The presented wavelet-based trend extraction model is applied to GPS static baseline solutions. The second approach in this paper is introduced to detect and remove cycle slip error which can be seen as a singularity in the GPS data. The propagation of singularities between the wavelets levels of decomposition is different from the propagation of noise. This characteristic is used to identify the singularities from noise. | ||
|
Statistics Article View: 126 PDF Download: 244 |
||
