Cooperative D-GNSS Aided with Multi Attribute Decision Making Module: A Rigorous Comparative Analysis

Thanassis Mpimis, Theodore T. Kapsis, Athanasios D. Panagopoulos and Vassilis Gikas
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Thanassis Mpimis: School of Rural and Surveying Engineering, National Technical University of Athens, 15780 Athens, Greece
Theodore T. Kapsis: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Athanasios D. Panagopoulos: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Vassilis Gikas: School of Rural and Surveying Engineering, National Technical University of Athens, 15780 Athens, Greece

Future Internet, 2022, vol. 14, issue 7, 1-16

Abstract: Satellite positioning lies within the very core of numerous Intelligent Transportation Systems (ITS) and Future Internet applications. With the emergence of connected vehicles, the performance requirements of Global Navigation Satellite Systems (GNSS) are constantly pushed to their limits. To this end, Cooperative Positioning (CP) solutions have attracted attention in order to enhance the accuracy and reliability of low-cost GNSS receivers, especially in complex propagation environments. In this paper, the problem of efficient and robust CP employing low-cost GNSS receivers is investigated over critical ITS scenarios. By adopting a Cooperative-Differential GNSS (C-DGNSS) framework, the target’s vehicle receiver can obtain Position–Velocity–Time (PVT) corrections from a neighboring vehicle and update its own position in real-time. A ranking module based on multi-attribute decision-making (MADM) algorithms is proposed for the neighboring vehicle rating and optimal selection. The considered MADM techniques are simulated with various weightings, normalization techniques, and criteria associated with positioning accuracy and reliability. The obtained criteria values are experimental GNSS measurements from several low-cost receivers. A comparative and sensitivity analysis are provided by evaluating the MADM algorithms in terms of ranking performance and robustness. The positioning data time series and the numerical results are then presented, and comments are made. Scoring-based and distance-based MADM methods perform better, while L1 RMS, HDOP, and Hz std are the most critical criteria. The multi-purpose applicability of the proposed scheme, not only for land vehicles, is also discussed.

Keywords: Intelligent Transportation Systems; Cooperative Positioning; low-cost GNSS; connected vehicles; multi-criteria decision making; ranking methods; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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