Unscented Kalman filter with nonlinear dynamic process modeling for GPS navigation |
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Authors: | Dah-Jing Jwo Chun-Nan Lai |
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Institution: | (1) Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, 2 Peining Rd., Keelung, 202-24, Taiwan |
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Abstract: | This paper preliminarily investigates the application of unscented Kalman filter (UKF) approach with nonlinear dynamic process
modeling for Global positioning system (GPS) navigation processing. Many estimation problems, including the GPS navigation,
are actually nonlinear. Although it has been common that additional fictitious process noise can be added to the system model,
however, the more suitable cure for non convergence caused by unmodeled states is to correct the model. For the nonlinear
estimation problem, alternatives for the classical model-based extended Kalman filter (EKF) can be employed. The UKF is a
nonlinear distribution approximation method, which uses a finite number of sigma points to propagate the probability of state
distribution through the nonlinear dynamics of system. The UKF exhibits superior performance when compared with EKF since
the series approximations in the EKF algorithm can lead to poor representations of the nonlinear functions and probability
distributions of interest. GPS navigation processing using the proposed approach will be conducted to validate the effectiveness
of the proposed strategy. The performance of the UKF with nonlinear dynamic process model will be assessed and compared to
those of conventional EKF. |
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Keywords: | Extended Kalman filter Unscented Kalman filter Nonlinear model Global positioning system (GPS) |
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