The amplitude-locked loop separation system using Kalman fuzzy algorithm [Kalman filter]

In this paper, we present a Kalman filter algorithm with fuzzy theory, which shows how to search for the optimum weight coefficient values of a Kalman filter. In the traditional Kalman filter algorithm, we discover some drawbacks such as low convergence time and large mean square error (MSE). Theref...

Full description

Saved in:
Bibliographic Details
Published inProceedings of 2004 International Symposium on Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004 pp. 516 - 519
Main Authors Gwo Jia Jong, Ci Fang Syu, Te Jen Su
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Additive white noise
AWGN
Convergence
Frequency
Fuzzy logic
Fuzzy systems
Interference
Kalman filters
Mathematical model
Phase locked loops
Online AccessGet full text

Cover

More Information
Summary:In this paper, we present a Kalman filter algorithm with fuzzy theory, which shows how to search for the optimum weight coefficient values of a Kalman filter. In the traditional Kalman filter algorithm, we discover some drawbacks such as low convergence time and large mean square error (MSE). Therefore, we adopt a Kalman algorithm combined with fuzzy logic theory for improving the convergence rate and the MSE value of the conventional Kalman filter using simulated results under an additive white gaussian noise (AWGN) channel. The major goal of this paper is to show that a Kalman filter, which is based on fuzzy logic for separating the signals under an AWGN channel, has better performance than the conventional Kalman algorithm.
ISBN:9780780386396
0780386396
DOI:10.1109/ISPACS.2004.1439109