Enhancements of the Range Consensus Algorithm (RANCO)

Abstract

In anticipation of the future GNSS constellations becoming operational, it will no longer be possible to assume that the probability of failure for more than one satellite within a certain timeframe is negligible. Further, it is questionable whether it is always reasonable to compute a position estimate based on all satellites in view, rather than selecting the “best” subset. The Range Consensus (RANCO) algorithm is not only capable of detecting multiple satellite failures at a time, but it also allows the determination of good estimates of the current ranging biases. RANCO calculates position solutions based on subsets of four satellites and compares this estimate with the pseudoranges of all the satellites not contributing to this solution. The residuals of this estimate are then used as a measure of statistical consensus. The scope of this work is the optimization of the performance of RANCO by restricting it to the detection of a certain number of failed satellites at a time and by finding an optimal subset selection process for this constraint. Furthermore, the computation of the subset quality was reconsidered and significantly improved by the use of the Weighted Dilution of Precision (WDOP). In this paper, the physical model for determining the threshold for the separation between correct and faulty satellite signals has been extended. The RANCO algorithm was also verified with respect to its capability of detecting and identifying satellites with a bias higher than a given threshold. Throughout the paper those satellites are defined to fail. The abilities of RANCO, to exclude multiple simultaneous ranging faults and low biases, paves the way for safety critical applications by combining receiver autonomous algorithms with the integrity channel information from future GNSS systems.