Document Type : Original Article

Authors

1 Faculty of Electrical & Computer engineering, Malek Ashtar University of technology, Iran.

2 Faculty of Electrical & Computer engineering, Malek Ashtar University of technology, Iran

10.22034/jast.2021.272551.1047

Abstract

This paper presents a new Modified Predictive Kalman Filter (MPKF). To solve the problem of a strap-down inertial navigation system (SINS) self-alignment process that the standard Kalman filters cannot give the optimal solution when the system model and stochastic information are unknown accurately. The proposed algorithm is applied to SINS in the initial alignment process with a large misalignment heading angle. The filter is based on the idea of an accurate predictive filter applies n-steps ahead prediction of the SINS model errors to effectively enhance the corrections of the current information residual error on the system. Firstly, the formulations of a novel predictive filter and a fine alignment algorithm for SINS are presented. Secondly, the vehicle results demonstrate the superior performance of the proposed method, in which the MPKF algorithm is less sensitive to uncertainty. It performs faster and more accurate estimation of SINS' initial orientation angles compared with the conventional EKF method.

Keywords

Main Subjects

Article Title [فارسی]

Design and Vehicle Test of a Modified Predictive Kalman Filter for SINS Self Accurate Initial Alignment

Author [فارسی]

  • نعمت الله قهرمانی 2

Abstract [فارسی]

This paper presents a new Modified Predictive Kalman Filter (MPKF). To solve the problem of a strap-down inertial navigation system (SINS) self-alignment process that the standard Kalman filters cannot give the optimal solution when the system model and stochastic information are unknown accurately. The proposed algorithm is applied to SINS in the initial alignment process with a large misalignment heading angle. The filter is based on the idea of an accurate predictive filter applies n-steps ahead prediction of the SINS model errors to effectively enhance the corrections of the current information residual error on the system. Firstly, the formulations of a novel predictive filter and a fine alignment algorithm for SINS are presented. Secondly, the vehicle results demonstrate the superior performance of the proposed method, in which the MPKF algorithm is less sensitive to uncertainty. It performs faster and more accurate estimation of SINS' initial orientation angles compared with the conventional EKF method.

Keywords [فارسی]

  • Modified Predictive Kalman Filter (MPKF)
  • Self Alignment
  • Strap-down Inertial Navigation System (SINS)
  • Large Heading Angle