A Hybrid Prediction Method for Bridging GPS Outages in High-Precision POS Application

Autor:	Jiancheng Fang, Linzhouting Chen
Rok vydání:	2014
Předmět:	Engineering Bridging (networking) Artificial neural network business.industry Remote sensing application Real-time computing GPS/INS Control engineering Kalman filter GPS signals Global Positioning System Electrical and Electronic Engineering business Instrumentation Inertial navigation system
Zdroj:	IEEE Transactions on Instrumentation and Measurement. 63:1656-1665
ISSN:	1557-9662 0018-9456
Popis:	Position and orientation system (POS) is a key technology widely used in remote sensing applications, which integrates inertial navigation system (INS) and GPS using a Kalman filter (KF) to provide high-accuracy position, velocity, and attitude information for remote sensing motion compensation. However, when GPS signal is blocked, the POS accuracy will decrease owing to the unbounded INS error accumulation. To improve the reliability and accuracy of POS, this paper proposes a hybrid prediction method for bridging GPS outages. This method uses radial basis function (RBF) neural network coupled with time series analysis to forecast the measurement update of KF, resulting in reliable performance during GPS outages. In verifying the proposed hybrid prediction method, a flight experiment was conducted in 2011, based on a high-precision Laser POS. Experimental results show that the proposed hybrid prediction method is more effective than two other methods (KF and RBF neural network).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::54dbbfb2e835fd4313dced3373ffb8a0 https://doi.org/10.1109/tim.2013.2292277 Zobrazit plný text záznamu