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Unscented Kalman filter with nonlinear dynamic process modeling for GPS navigation

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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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Acknowledgments

Funding for this work was provided by the National Science Council of the Republic of China under grant numbers NSC 95-2221-E-019-026 and NSC 96-2221-E-019-007. The authors gratefully acknowledge the support. Efforts made by Shih-Yao Lai and Guo-Sheng Shieh on simulation implementation for the revised version are also gratefully acknowledged.

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Authors and Affiliations

  1. Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, 2 Peining Rd., Keelung, 202-24, Taiwan

    Dah-Jing Jwo & Chun-Nan Lai

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  1. Dah-Jing Jwo
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  2. Chun-Nan Lai
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Correspondence to Dah-Jing Jwo.

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Jwo, DJ., Lai, CN. Unscented Kalman filter with nonlinear dynamic process modeling for GPS navigation. GPS Solut 12, 249–260 (2008). https://doi.org/10.1007/s10291-007-0081-9

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