Wavelet Neural Network employment for continuous GNSS orbit function construction: Application for the Assisted-GNSS principle

Authors:
P. PavlovčIč PrešEren;B. Stopar
Affiliations:
University of Ljubljana, Jamova 2, 1000 Ljubljana, Slovenia;University of Ljubljana, Jamova 2, 1000 Ljubljana, Slovenia
Venue:
Applied Soft Computing
Year:
2013

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Abstract

This paper presents a Wavelet Neural Network (WNN) employment for discrete precise ephemerides tabular data of Global Navigation Satellite System (GNSS) orbit approximation to obtain continuous orbit function. Orbit function is essential in positioning and navigation tasks, the advantage of continuity, however, is that it can also be used during GNSS signal interruptions. The essence of WNN continuous orbit construction is single function determination for the entire interval, while the interpolation methods follow several discrete function establishment. Specifically, we investigate the performance of the WNN continuous orbit approximation by comparison with well known polynomial and trigonometric interpolations. The experimental results show that our proposed method is superior to the traditional methods especially near the end of intervals, because they are not subject to large scale function oscillations as in the case of polynomials constructions. We propose a WNN construction using different mother functions of the WNN namely Mexican hat, Morlet function, Gaussian and Daubechies (D4) wavelet. Furthermore best algorithm for regression estimation is described; selection of neurons in the hidden layer of WNN is based on orthogonal least squares algorithm. The main objective of this article is to show that the presented method of orbit function construction could be used for GNSS ephemerides distribution and short-time prediction in the Assisted GNSS-networks.