Statistical Error Calibration for Hatch-filtered Ranges using Spectral Characterization of Raw Measurement Errors

Johannes Mach, Ingrid Deuster, Robert Wolf

Abstract:	The presented error calibration concept for Hatch-filtered ranges makes use of a recursive error propagation algorithm, which is based on the covariance analysis for a Kalman filter with sub-optimal gain filters. This approach naturally covers all possible geometries and line-of-sight histories, because the error propagator can always be updated in parallel with the actual Hatch filter. Under the condition that the underlying stochastic model is representative for the true error behavior, the error propagation algorithm produces optimal a-priori error estimates, also before filter convergence. In addition a suitable calibration method has been developed that allows to determine the necessary stochastic model parameters as a function of elevation. The calibration includes a simple but efficient characterization of spectral properties. It is based on raw pseudo-range data rather than filtered ranges. This is another important advantage, because the calibration does not have to be repeated in case of change of algorithm parameters (e.g. filter constant). Due to these advantages the concept is considered an interesting alternative to the usual direct calibration of smoothed range errors. Possible fields of application are user positioning as well as system monitoring algorithms.
Published in:	Proceedings of the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2007) September 25 - 28, 2007 Fort Worth Convention Center Fort Worth, TX
Pages:	306 - 311
Cite this article:	Mach, Johannes, Deuster, Ingrid, Wolf, Robert, "Statistical Error Calibration for Hatch-filtered Ranges using Spectral Characterization of Raw Measurement Errors," Proceedings of the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2007), Fort Worth, TX, September 2007, pp. 306-311.
Full Paper:	ION Members/Non-Members: 1 Download Credit Sign In