A Comparison between Kalman Filter and Linear Prediction Coefficients Technique

Authors

  • Zainab Abdul Redha

Abstract

Modern estimation methods have become more important as they have been in past years, due to the expansion of the field of science and technology and the increasing in data. So, the attention has become to estimation methods that solving the noise problems that occur in data. The Kalman filter has become one of the most widespread and most reliable estimators in the event that there is noise in the data. This paper tests the use of the Kalman filter to estimate the data that contains noise and compare the results with the linear prediction coefficient. The data is randomly generated in a simulation study. The results shows that Kalman is more efficient at filtering noise from the data and giving less mean square error in estimation results than the other.

Published

2020-12-30

Issue

Section

Articles