Monitoring processes using sensor networks and an extended Kalman filter

Authors:
Richard Wasniowski
Affiliations:
Computer Science Department, California State University, Carson, CA
Venue:
CONTROL'05 Proceedings of the 2005 WSEAS international conference on Dynamical systems and control
Year:
2005

Citing 4
Cited 1

Kalman filtering with real-time applications

Kalman filtering with real-time applications
Adaptive filter theory (3rd ed.)

Adaptive filter theory (3rd ed.)
Exponentiated gradient versus gradient descent for linear predictors

Information and Computation
Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach

Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach

Data fusion and topology control in wireless sensor networks

WSEAS Transactions on Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Two main difficulties in process monitoring are lack of reliable measurements of key process variables and difficulty in defining quantitative relationships between state variables. In this study sensor networks are used to demonstrate an approach based on Kalman filtering to model the specific monitoring systems. Kalman filtering at both local nodes and fusion center are the covariance matrices of tracking errors. Performance analysis is dedicated to the distributed Kalman filtering fusion for distributed recursive state estimators of dynamic systems under consideration.