A Bayesian method for linear, inequality-constrained adjustment and its application to GPS positioning

Authors: Zhu, JianjunSanterre, RockChang, Xiao-Wen
Abstract: One of the typical approaches to linear, inequality-constrained adjustment (LICA) is to solve a least-squares (LS) problem subject to the linear inequality constraints. The main disadvantage of this approach is that the statistical properties of the estimate are not easily determined and thus no general conclusions about the superiority of the estimate can be made. A new approach to solving the LICA problem is proposed. The linear inequality constraints are converted into prior information on the parameters with a uniform distribution, and consequently the LICA problem is reformulated into a Bayesian estimation problem. It is shown that the LS estimate of the LICA problem is identical to the Bayesian estimate based on the mode of the posterior distribution. Finally, the Bayesian method is applied to GPS positioning. Results for four field tests show that, when height information is used, the GPS phase ambiguity resolution can be improved significantly and the new approach is feasible.
Document Type: Article de recherche
Issue Date: 29 January 2005
Open Access Date: 13 January 2017
Document version: VoR
Permalink: http://hdl.handle.net/20.500.11794/13069
This document was published in: Journal of Geodesy, Vol. 78 (9), 528–534 (2005)
Alternative version: 10.1007/s00190-004-0425-y
Collection:Articles publiés dans des revues avec comité de lecture

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