NDLI: Self-tuning full-order WMF Kalman filter for multisensor descriptor systems

Content Provider	IET Digital Library
Author	Dou, Yinfeng Sun, Shuli Ran, Chenjian
Abstract	For the multisensor descriptor systems with correlated measurement noises and unknown noise variances, a self-tuning full-order weighted measurement fusion (WMF) Kalman filter is presented. First, based on the controlled autoregressive moving average innovation model of the multisensor descriptor systems, the consistent estimates of the unknown noise variances are obtained applying correlation function method. A compressed measurement equation for the multisensor descriptor systems is obtained by the WMF method, and an optimal full-order WMF descriptor Kalman filter is given. Based on the optimal full-order WMF Kalman filter and the estimates of noise variances, a self-tuning full-order WMF Kalman filter with the self-tuning descriptor Riccati equation is presented. By the dynamic variance error system analysis method, it is proven that the solution of the self-tuning descriptor Riccati equation converges to the solution of the optimal descriptor Riccati equation. Then, the convergence of the presented self-tuning full-order WMF Kalman filter is proven. A simulation example of a six-sensor descriptor system verifies the effectiveness and convergence of the presented algorithms.
Starting Page	359
Ending Page	368
Page Count	10
ISSN	17518644
Volume Number	11
e-ISSN	17518652
Issue Number	Issue 3, Feb (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/11/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2016.0803
Journal	IET Control Theory & Applications
Publisher Date	2016-11-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Autoregressive Moving Average Processes Compressed Measurement Equation Controlled Autoregressive Moving Average Innovation Model Correlated Measurement Noises Correlation Function Method Correlation Method Dynamic Variance Error System Analysis Method Error Analysis Error Analysis in Numerical Method Filtering Method in Signal Processing Kalman Filter Linear Algebra Measurement Error Multisensor Descriptor System Numerical Analysis Optimal Full-order WMF Descriptor Kalman Filter Riccati Equation Self-tuning Descriptor Riccati Equation Self-tuning Full-order Weighted Measurement Fusion Self-tuning Full-order WMF Kalman Filter Sensor Fusion Six-sensor Descriptor System Statistics Unknown Noise Variance Estimation
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Human-Computer Interaction Electrical and Electronic Engineering Computer Science Applications

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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