A NOVEL APPROACH TO STATE SPACE TIME DOMAIN AUTOREGRESSIVE SIGNAL PROCESSING USING OPTIMAL RECURSIVE ESTIMATOR

Authors

  • Jawahar A Department of EEE, Sanketika Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, India.
  • Murali Krishna P Department of Electrical Engineer, National Operation and Maintenance Company Limited, Jeddah, Saudi Arabia.
  • Kiran Ss Department of ECE, Sanketika Institute of Technology and Management, Visakhapatnam, Andhra Pradesh, India.

Abstract

This work describes the concept of filtering of signals using discrete Kalman filter. The true state of constant, random constant having process noise and autoregressive (p) process when corrupted by measurement noise are estimated using discrete Kalman filter and results are presented using MATLAB.

References

Simon D. Optimal State Estimation Kalman, H Infinity, and Non-linear Approaches. Hoboken: John Wiley and Sons, INC; 2006.

Monson H. Hayes, Statistical Digital Signal Processing and Modeling. New York, NY: John Wiley and Sons, INC; 1996.

Diniz PS. Adaptive Filtering Algorithms and Practical Implementation. New York: Kluwer Academic Publishers; 2013.

Haykin S. Kalman Filtering And Neural Networks. New York: John Wiley & Sons, INC; 2001.

Deller JR, Hansen JH, Proakis JG. Discrete Time Processing of Speech Signals. New York: The Institute of Electrical and Electronic Engineers INC; 1993.

Published

01-07-2018

How to Cite

A, J., P, M. K., & Ss, K. (2018). A NOVEL APPROACH TO STATE SPACE TIME DOMAIN AUTOREGRESSIVE SIGNAL PROCESSING USING OPTIMAL RECURSIVE ESTIMATOR. Innovare Journal of Engineering and Technology, 6(1), 6–9. Retrieved from https://mail.innovareacademics.in/journals/index.php/ijet/article/view/16054

Issue

Vol 6 Issue 1, 2018 (Jul-Aug)

Section

Review Article(s)