NDLI: Iteratively reweighted correlation analysis method for robust parameter identification of multiple-input multiple-output discrete-time systems

Content Provider	IET Digital Library
Author	Wang, Zhu Jin, Qibing Liu, Xiaoping
Abstract	In the engineering practices, the distributions of measurements are non-Gaussian as they contain outliers. As some slight deviations from the Gaussian assumption would probably cause the performance of an estimator to degrade significantly, a novel iteratively reweighted correlation analysis method is proposed for robust parameter estimation of multiple-input multiple-output (MIMO) systems, in the presence of Student's t-noises. The iterative method achieves good robustness and high efficiency by the combination of multivariable correlation analysis and t-distribution based M-estimators. The appropriate updating weights are able to enter into the sample cross-correlation function, so that the heavy tails are lowered, and the impact of outliers is weakened to the greatest extent. Based on the robust finite impulse response models, the identification procedure is then to reconstruct the noise-free estimates to identify the parameters of an MIMO system. The theoretical discussions and simulation results demonstrate that the proposed method works well.
Starting Page	549
Ending Page	556
Page Count	8
ISSN	17519675
Volume Number	10
e-ISSN	17519683
Issue Number	Issue 5, Jul (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/10/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2015.0279
Journal	IET Signal Processing
Publisher Date	2016-07-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Correlation Method Interpolation And Function Approximation Iterative Method Iteratively Reweighted Correlation Analysis Method MIMO Communication MIMO System Robust Parameter Estimation Multiple-input Multiple-output Discrete-time System Multivariable Correlation Analysis Noise-free Estimate Reconstruction NonGaussian Measurement Distribution Numerical Analysis Parameter Estimation Radio Link And Equipment Robust Finite Impulse Response Model Robust Parameter Identification Sample Cross-correlation Function Signal Processing And Detection Student T-noise T-distribution Based M-estimator Transient Response
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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