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Precision-Aided Partial Ambiguity Resolution Technique for Short to Medium Baseline Positioning

Medina, Daniel and Castro Arvizu, Juan Manuel and Vilà-Valls, Jordi and Closas, Pau and Ziebold, Ralf and Chaumette, Eric (2020) Precision-Aided Partial Ambiguity Resolution Technique for Short to Medium Baseline Positioning. Aeroconf 2021, Big Sky, Montana (US).

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GNSS carrier phase observations are fundamental for safety-critical applications where the requirements of accuracy and availability are stringent. Unlike code observations, carrier phase measurements pose high precision at the cost of being ambiguous, since only their fractional part is measured by the receiver. The unknown number of integer cycles, the so-called ambiguities, is to be determined jointly to the dynamical parameters of the target and leads to the mixed real and integer parameter estimation problem. Although the mixed-integer estimation problem has been widely studied, its performance is subject to certain unresolved challenges. For instance, upon the deployment of new GNSS constellation and frequencies, the large number of observations can lead to a decreased probability of successfully mapping the real-valued carrier phase ambiguities to integer ones. In this regard, Partial Ambiguity Resolution (PAR) relaxes the condition of fixing the complete set of ambiguities and finds instead a subset of these based on the optimization of certain objective function (i.e., maximizing the probability of ambiguity resolution success rate). Existing PAR approaches prioritize the chance of finding an integer solution, regardless of whether the posterior positioning result presented a poor accuracy. Such phenomena occur when, for instance, solely a few ambiguities are fixed. To amend this common PAR-related issue, this work introduces the precision-aided PAR, for which the projection of the estimated ambiguities into the position domain is exploited to guide the PAR ambiguity selection process. The premise is straightforward: the covariance matrix for the (fixed) position solution is used to pose a constraint on the integer resolution minimization problem, based on a goal accuracy to achieve. Precision-guided PAR allows to efficiently working with large number of subsets, which makes the algorithm attractive for multi-GNSS multi-frequency applications with stringent availability and accuracy requirements. We leverage on the recently proposed Cramér Rao Bound (CRB) for the mixed-integer model to characterize what is the minimal achievable performance for a subset of observations. The experimentation comprises a synthetic scenario where the performance comparison for Full Ambiguity Resolution (FAR), classical PAR based on sequential observation elimination and the proposed precision-aided PAR is addressed. The evaluation employs multi-GNSS (GPS, Galileo and BeiDou) triple frequency observations and a variety of possible lengths.

Item URL in elib:https://elib.dlr.de/136350/
Document Type:Conference or Workshop Item (Lecture)
Title:Precision-Aided Partial Ambiguity Resolution Technique for Short to Medium Baseline Positioning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Medina, DanielUNSPECIFIEDhttps://orcid.org/0000-0002-1586-3269UNSPECIFIED
Vilà-Valls, JordiUNSPECIFIEDhttps://orcid.org/0000-0001-7858-4171UNSPECIFIED
Closas, PauUNSPECIFIEDhttps://orcid.org/0000-0002-5960-6600UNSPECIFIED
Date:8 October 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:GNSS; PAR; CRB; Mixed Model;
Event Title:Aeroconf 2021
Event Location:Big Sky, Montana (US)
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Communication and Navigation
DLR - Research area:Raumfahrt
DLR - Program:R KN - Kommunikation und Navigation
DLR - Research theme (Project):R - Project Navigation 4.0 (old)
Location: Neustrelitz
Institutes and Institutions:Institute of Communication and Navigation > Nautical Systems
Deposited By: Medina, Daniel
Deposited On:08 Oct 2020 13:50
Last Modified:14 Jan 2022 10:14

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