A Comparison of Ambiguity Resolution Methods for RTK and PPP-IAR under Challenging Environments

Viet Duong

Abstract: The successful determination of the initial integer ambiguity parameters is a key prerequisite for fast and reliable solution convergence time in both Real-Time-Kinematic (RTK) and Precise Point Positioning with integer ambiguity resolution (PPP-IAR). However, confidently resolving the integer ambiguities, especially in complex environments, remains a tricky problem. When the success rate using the integer least squares (ILS) resolved by means of the LAMBDA method is too low, the best integer equivariant (BIE) estimator can be an alternative. The BIE estimator has been proven to have a minimal mean square error (MSE) as compared to that from the float least-squares estimator and ILS solutions. In this contribution, four different ambiguity resolution methods based on (a) the BIE estimator using the multivariant t-distribution function (BIEE-TD), (b) the BIE estimator using the multivariate normal distribution function (BIEE-GA), (c) the partial ambiguity resolution based on integer bootstrapping (PAR-BS), and (d) the ILS employing both the ratio test and the fixed failure rate are compared in both RTK and PPP-IAR technique. Two different GNSS datasets were collected under high-multipath conditions in 2022 for RTK and PPP-IAR. They were then processed to determine the positioning accuracy performance for each ambiguity resolution method. It is found that the BIE using the multivariate t-distribution provides better positioning accuracy compared to that using normal distribution, PAR-BS and the ILS.
Published in: Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023)
September 11 - 15, 2023
Hyatt Regency Denver
Denver, Colorado
Pages: 1 - 11
Cite this article: Duong, Viet, "A Comparison of Ambiguity Resolution Methods for RTK and PPP-IAR under Challenging Environments," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 1-11. https://doi.org/10.33012/2023.19178
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