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Sensors 2018, 18(11), 3809; https://doi.org/10.3390/s18113809

A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System

1
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
2
State Key Laboratory of Satellite Navigation System and Equipment Technology, Shijiazhuang 050081, China
3
State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130025, China
*
Author to whom correspondence should be addressed.
Received: 7 October 2018 / Revised: 30 October 2018 / Accepted: 1 November 2018 / Published: 6 November 2018
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Abstract

Recently, the integration of an inertial navigation system (INS) and the Global Positioning System (GPS) with a two-antenna GPS receiver has been suggested to improve the stability and accuracy in harsh environments. As is well known, the statistics of state process noise and measurement noise are critical factors to avoid numerical problems and obtain stable and accurate estimates. In this paper, a modified extended Kalman filter (EKF) is proposed by properly adapting the statistics of state process and observation noises through the innovation-based adaptive estimation (IAE) method. The impact of innovation perturbation produced by measurement outliers is found to account for positive feedback and numerical issues. Measurement noise covariance is updated based on a remodification algorithm according to measurement reliability specifications. An experimental field test was performed to demonstrate the robustness of the proposed state estimation method against dynamic model errors and measurement outliers. View Full-Text
Keywords: two-antenna GPS/INS; navigation system; adaptive noise covariance; measurement outliers; positive feedback; numerical issue two-antenna GPS/INS; navigation system; adaptive noise covariance; measurement outliers; positive feedback; numerical issue
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Hao, Y.; Xu, A.; Sui, X.; Wang, Y. A Modified Extended Kalman Filter for a Two-Antenna GPS/INS Vehicular Navigation System. Sensors 2018, 18, 3809.

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