Evidential framework for robust localization using raw GNSS data. (May 2017)

• Record Type: Journal Article
• Authors: Zair, Salim; Le Hégarat-Mascle, Sylvie
• Is Part Of: Engineering applications of artificial intelligence. Volume 61(2017:Jan.)
• Page Start: 126
• Page End: 135
• Publication Date: 2017-05
• Subjects: Global navigation satellite systems -- Outlier detection -- Belief function theory -- Consistency measure; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.02.003
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Global Navigation Satellite Systems (GNSS) positioning in constrained environments suffers from the presence of Non Line Of Sight and multipath receptions so that, in addition to contain imprecise measurements (e.g. due to atmospheric effects), the set of GNSS pseudo-range observations includes some outliers. In this study, we evaluate the interest of the belief function framework as an alternative to the Interval Analysis approach classically used in localization to deal with imprecise data and possibly outliers. Following the basic idea of the RANSAC (RANdom SAmpling Consensus) algorithm, we propose a new detection of the outliers based on an evidential measure of the consistency of the solution. Each pseudo-range (PR) observation generates a 2D basic belief assignment (bba) that quantifies, for any 2D set, the possibility (according to the observed PR) that it includes the GNSS receiver. Outlier detection is then performed by evaluating directly the consistency of subsets of bbas and the inlier PR information is aggregated through the combination of corresponding bbas. In the case of a dynamic receiver, filtering is performed by combining the bbas derived from the new observations to a bba predicted from estimation at previous time step. Proposed approach was evaluated on two actual datasets acquired in urban environment. Results are evaluated both in terms of precision of the localization and in terms of guarantee of the solution. They compared with former approaches either in belief function framework or using interval analysis, stating the interest of the proposed algorithm. Abstract : Highlights: New algorithm to detect the outliers in Belief Function Theory (BFT) framework. Comparison of the proposed algorithm for outlier detection with evidential q-relaxation proposed in BFT. New GNSS localization algorithm robust to outliers in pseudo-range observations. Validation of the proposed GNSS-only localization in constrained environments. Results analysed both in terms of localization precision and of solution guarantee.