A simple recursive method for stationary receiver position estimation using GPS difference measurements

Canelon, Jose I.; Provence, Robert S.; Shieh, Leang-San; Liu, Ce R.

doi:10.1016/j.isatra.2006.09.003

Cited by 3 publications

(1 citation statement)

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“…The UKF is more accurate than the EKF when applied to nonlinear systems (Canelon et al, 2007; Grewal and Andrews, 2011; Houshangi and Azizi, 2005), however, in systems with high dimensions, due to the increase of the computation complexity, the filter convergence would be extremely slow. Moreover, to achieve a proper estimation, the tuning parameters of the UKF should be accurately adjusted.…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement for Mobile Robot Position Determination Using Cubature Kalman Filter

Zarei

Ramezani

2017

J. Navigation

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The objective of this paper is to accurately determine mobile robots' position and orientation by integrating information received from odometry and an inertial sensor. The position and orientation provided by odometry are subject to different types of errors. To improve the odometry, an inertial measurement unit is exploited to give more reliable attitude information. However, the nonlinear dynamic of these systems and their complexities such as different sources of errors make navigation difficult. Since the dynamic models of navigation systems are nonlinear in practice, in this study, a Cubature Kalman Filter (CKF) has been proposed to estimate and correct the errors of these systems. The information from odometry and a gyroscope are integrated using a CKF. Simulation results are provided to illustrate the superiority and the higher reliability of the proposed approach in comparison with conventional nonlinear filtering algorithms such as an Extended Kalman Filter (EKF).

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Section: Introductionmentioning

confidence: 99%