Title: Modeling of Nonlinear Errors for Integrated GPS/MEMS-based INS Navigation Systems
Author(s): Maged Ismail, Ezzeldin Abdelqawey, Nesreen I. Ziedan
Published in: Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016)
September 12 - 16, 2016
Oregon Convention Center
Portland, Oregon
Pages: 3598 - 3606
Cite this article: Ismail, Maged, Abdelqawey, Ezzeldin, Ziedan, Nesreen I., "Modeling of Nonlinear Errors for Integrated GPS/MEMS-based INS Navigation Systems," Proceedings of the 29th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2016), Portland, Oregon, September 2016, pp. 3598-3606.
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Abstract: Vehicle navigation in urban areas needs strongly to a system that guarantees smooth maneuvering and safe arrival to objectives. In addition to this accuracy demand, the used navigation system should meet other requirements such as lightness in weight, high reliability, smallness and low cost. Later requirements were achieved, to some extent, by MEMS IMU sensors while the accuracy requirement has been the objective for many researchers in last decade. Integration with GPS was a powerful tool to correct the MEMS-based inertial measurement unit to achieve more robust positioning and orientation information. In many GPS/INS integration schemes, the linear errors of MEMS IMU were estimated precisely while the higher order errors were ignored. Some researches were focused on estimating the higher order (nonlinear) errors by using nonlinear estimators such as particle filter while the other researchers used nonlinear autoregressive models, augmented with Kalman filter, to estimate both linear and nonlinear inertial errors and reduce them. In this paper, we introduce a Kalman filter augmented with nonlinear autoregressive modeling algorithms (NLARX) to estimate the MEMS-based IMU errors in order to achieve enhanced performance of integrated INS/GPS system. A comparison study of the proposed method against different augmented approaches as well as particle filtering is discussed with details. Experimental results depicted here, show that the proposed method introduces promising results to reduce the MEMS IMU errors compared with competing algorithms. The proposed integration mechanism depends on reduced number of low cost MEMS-based inertial sensors integrated with GPS receiver. The experimental work showed that the corrections predicted by the sigmoid net and wavenet NLARX models during GPS outages enhance the positional accuracy. As a result, the proposed KF/NLARX method is capable of modeling and suppression of the azimuth nonlinear and residual errors after the KF prediction, which could not be evaluated by the KF-only solution linearized error models and its GM model.