A comparison between resistant GNSS positioning techniques in harsh environment

Abstract

Environments as urban areas are critical for GNSS, because several obstacles block, attenuate and distort the signals; consequently, frequent blunders are present among the measurements and their effect on the position could be harmful. Two approaches are usually adopted to tackle the blunder issue, RAIM and robust estimation, and both are effective in case of high redundancy and single blunders. An alternative method, based on bootstrapping, i.e. random sampling with replacement, the available measurements, has recently emerged. The performance of the considered methods could be augmented by exploiting suitable measurement error models, which are used to differently weighting the measurements in RAIM and robust estimators, and to defining not uniform sampling probabilities in bootstrap; several models, based on the most common measurement quality indicators, carrier-to-noise ratio and satellite elevation, are herein analyzed. In this work, the three techniques, coupled with several error models, are compared in terms of mean, RMS and maximum position errors, processing data from urban scenario. The results demonstrate the best performance of bootstrap method, which works effectively in case of multiple blunders and/or the lack of redundancy, when RAIM and robust techniques are often unsuccessful. Moreover, the results highlight the importance of a careful choice of a measurement error model.