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• W2970086804 abstract "Abstract Low‐cost global positioning system (GPS) receivers installed in most vehicles and smart devices typically display tens of meters of error in their location data. A large amount of research effort has been made to increase the positioning accuracy of GPS receivers mostly by reducing errors with the help of auxiliary devices and/or methodologies such as differential GPSs, assisted GPSs, real‐time kinematic positioning, computer vision, etc, which result in a certain amount of cost increase. In this paper, we propose a new cooperative vehicular localization scheme based on vehicle‐to‐vehicle communication. The proposed scheme first estimates the distances between neighboring vehicles using the weighted least square of double difference scheme and uses a novel machine learning technique called constrained self‐organizing map (C‐SOM) with a set of adjusted GPS fixes to generate the final estimates of GPS locations with considerably lower errors. We also propose a new method of presenting training samples to address the issue of convergence failure that arises from the constraints imposed on the inter‐vehicular distances. We present simulation results that demonstrate the superior performance of the proposed scheme over both the conventional SOM and the distributed location estimate algorithm." @default.
• W2970086804 created "2019-09-05" @default.
• W2970086804 creator A5009950592 @default.
• W2970086804 creator A5037586267 @default.
• W2970086804 date "2019-09-01" @default.
• W2970086804 modified "2023-10-16" @default.
• W2970086804 title "Improving positioning accuracy based on self‐organizing map (SOM) and inter‐vehicular communication" @default.
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• W2970086804 doi "https://doi.org/10.1002/ett.3733" @default.
• W2970086804 hasPublicationYear "2019" @default.
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