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Chengfa Gao

Researcher at Southeast University

Publications -  53
Citations -  491

Chengfa Gao is an academic researcher from Southeast University. The author has contributed to research in topics: Global Positioning System & GNSS applications. The author has an hindex of 10, co-authored 43 publications receiving 342 citations.

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An In-Vehicle Smartphone RTK/DR Positioning Method Combined with OSM Road Network

TL;DR: In this paper , the authors proposed an RTK/DR positioning method combined with the OpenStreetMap road network data to correct the heading angle during the linear motion phase to improve heading angle accuracy.
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Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints

TL;DR: In this paper , an inequality constraint method was introduced and verified in order to improve smartphone GNSS positioning performance using extra inequality information, and three constraint applications were derived from it, namely, vertical velocity, direction, and distance constraints.
Journal ArticleDOI

Enhanced neural network model for regional ionospheric modeling and evaluation under different solar-geomagnetic conditions

TL;DR: In this article , an enhanced neural network (ENN) model is proposed to capture the changing characteristics of ionospheric VTEC and compared with the traditional mathematical models, i.e., the POLYnomial (POLY) model, generalized trigonometric series function and spherical harmonic function (SHF) model.
Proceedings ArticleDOI

Smartphone High-Precision Positioning Based Vehicle Driving Warning System

TL;DR: In this article , a vehicle driving early warning system based on smartphone high-precision positioning is proposed, which can determine the lane position of each vehicle, and release vehicle safe driving warning information in real time through the vehicle information of the whole road network on the cloud platform.
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An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

TL;DR: In this paper, the authors proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the traditional MHM algorithm, which can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution.