| Summary: | 碩士 === 國立成功大學 === 測量及空間資訊學系碩博士班 === 96 === In the navigation and kinematic positioning field, the extended Kalman filter (EKF) has become the only and best way to develop the core algorithm of the integrated systems. However, the process models and measurement models of the integrated navigation systems are all nonlinear. In this case, when the local linearity assumption breaks down, i.e, the effects of the higher order terms of the Taylor series expansion become significant, this can seriously affect the accuracy or even lead to divergence of any inference system that is based on the EKF or that uses the EKF as a component part.
Recently, using the unscented Kalman filter (UKF), a systematic and nonlinear model, has become a significant method to research the nonlinear estimation. UKF requires no analytic derivation of Jacobian matrix or Hessians matrix as in the EKF. In a wide range of applications in the areas of nonlinear state estimation, UKF method is superior to standard EKF based estimation approaches.
Based on these reasons, this paper proposes the development of the algorithm of the UKF based loosely coupled INS/GPS integrated POS and also the analysis of the accuracy improvement of the UKF compared to the EKF in the integrated navigation system. The experimental results prove that the applications of UKF in loosely coupled INS/GPS integrated system would obtain higher accuracy solutions. Also, the analysis shows that the EKF needs to use specific process models to procedure the larger errors of the alignment. However, it is unnecessary for the UKF to have extra process models, and it could demonstrated the UKF’s capability of dealing with large and small attitude errors seamlessly.
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