Hyun-Su Hong

A leveling algorithm for an underwater vehicle using extended Kalman filter

Recently, the strapdown Attitude Reference System (ARS) became popular as an economic system for a small, light, low-cost system like an underwater vehicle. The ARS provides attitude information updated from the initial attitude. So, the initial attitude errors have a great effect on the ARS. In this paper, the leveling algorithm for compensating initial attitude errors using inertial sensors and a speed log is presented. The meaning of leveling in this paper is to acquire the two attitude angles of roll and pitch of the vehicle during its motion. The linear system model for the leveling is derived in order to apply extended Kalman filter (EKF) which is known to have many desirable properties. The simulation shows that the leveling algorithm using EKF is adequate by virtue of its property of decreasing attitude errors.

An Extended Kalman Filter Robust to Linearization Error

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.