SIMULASI FILTER KALMAN UNTUK ESTIMASI SUDUT DENGAN MENGGUNAKAN SENSOR GYROSCOPE

Abstract

Abstact The Kalman filter is a recursive solution to the process linear filtering problem that can remove the noise from signal and then the information can useful. The process that use Kalman filter must be approximated as two equations of linear system, state equation and output equation. Computation of Kalman filter is minimizes the mean of the square error. This paper explore the basic consepts of the Kalman filter algorithm and simulate its to filter data of gyroscope to get a rotation. The measurement noise covariance determines how much information from the sample is used. If measurement noise covariance is high show that the measurement isn’t very accurate. The process noise covariance contributes to the overall uncertainty of the estimate as it is added to the error covariance matrix in each time step. If the error covariance matrix is small the Kalman filter incorporates a lot less of the measurement into estimate of rotation. Key words: Kalman filter, linear system, gyroscope