State Estimation for Sampled-Data Descriptor Nonlinear System: A Strong Tracking Unscented Kalman Filter Approach

This paper proposes a state estimation method for a sampled-data descriptor system by the Kalman filtering method. The sampled-data descriptor system is firstly discretized to obtain a discrete-time nonsingular model. Based on the discretized nonsingular system, a strong tracking unscented Kalman fi...

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Bibliographic Details
Published in:Mathematical problems in engineering 2017-01, Vol.2017 (2017), p.1-9
Main Authors: Liang, Tiantian, Zhou, Zhenhua, Wang, Mao
Format: Article
Language:English
Subjects:
Aircraft
Algorithms
Computer simulation
Covariance
Discretization
Economic models
Kalman filters
Linear algebra
Mathematical problems
Noise
Nonlinear systems
Process controls
Signal processing
State estimation
System theory
Systems science
Tracking
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Summary:This paper proposes a state estimation method for a sampled-data descriptor system by the Kalman filtering method. The sampled-data descriptor system is firstly discretized to obtain a discrete-time nonsingular model. Based on the discretized nonsingular system, a strong tracking unscented Kalman filter (STUKF) algorithm is designed for the state estimation. Then, a defined suboptimal fading factor is proposed and added to the prediction covariance for decreasing the weight of the prior knowledge on the conventional UKF filtering solution. Finally, a simulation example is given to show the effectiveness of the proposed method.
ISSN:1024-123X
1563-5147
DOI:10.1155/2017/5640309