GPS Cycle Slip Detection and Correction using Bayesian Networks

The use of GPS for precise positioning applications requires the use of carrier-phase measurements. Integer ambiguities in the phase data must be removed to utilize the full measurement strength of the phase observable. This consists of initial integer ambiguities and additional integer ambiguities introduced by cycle slips. A slip of only a few cycles can bias measurements enough to make centimetre-level positioning or navigation difficult. Over the past decade a number of methods have been developed to detect and repair cycle slips. The majority of approaches involve forming cycle-slip-sensitive linear combinations of the available observables. Algorithms have been designed to detect, determine, and repair these cycle slips by fitting functions to the linear combinations and observing differences between the functions and the data combinations. These methods invariably require user intervention for problematic cycle slips in portions of data and tuning of input parameters to data. This paper adopts an increasingly important technique in the entire field of artificial intelligence namely Bayesian Networks (BN), as a powerful empirical modeling approach and yet relatively simple compared to other mathematical models, to detect and estimate the number of GPS cycle slip