Modern control systems heavily relay on sensors for closed-loop feedback control. Degradation of sensor performance due to sensor aging affects the closed-loop system performance, reliability, and stability. Sensor aging characterized by the sensor measurement noise covariance. This paper proposes an algorithm used to identify the slow varying sensor noise covariance online based on system sensor measurements. The covariance-matching technique, along with the adaptive Kalman filter is utilized based on the information about the quality of weighted innovation sequence to estimate the slow time-varying sensor noise covariance. The sequential manner of the proposed algorithm leads to significant reduction of the computational load. The covariance-matching of the weighted innovation sequence improves the prediction accuracy and reduces the computational load, which makes it suitable for online applications. Simulation results show that the proposed algorithm is capable of estimating the slow time-varying sensor noise covariance for MIMO systems with white noise whose covariance varies linearly, exponentially, or linearly with added sinusoid perturbation. Furthermore, the proposed estimation algorithm shows a reasonable convergence rate.
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ASME 2017 Dynamic Systems and Control Conference
October 11–13, 2017
Tysons, Virginia, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-5828-8
PROCEEDINGS PAPER
Online Sensor Noise Covariance Identification Using a Modified Adaptive Filter
Aqeel Madhag,
Aqeel Madhag
Michigan State University, East Lansing, MI
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Guoming George Zhu
Guoming George Zhu
Michigan State University, East Lansing, MI
Search for other works by this author on:
Aqeel Madhag
Michigan State University, East Lansing, MI
Guoming George Zhu
Michigan State University, East Lansing, MI
Paper No:
DSCC2017-5075, V002T04A001; 8 pages
Published Online:
November 14, 2017
Citation
Madhag, A, & Zhu, GG. "Online Sensor Noise Covariance Identification Using a Modified Adaptive Filter." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications. Tysons, Virginia, USA. October 11–13, 2017. V002T04A001. ASME. https://doi.org/10.1115/DSCC2017-5075
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