NDLI: Optimal quantisation for random parameter estimation

Content Provider	IET Digital Library
Author	Wu, Hao
Abstract	In this study, the optimal quantiser design for random parameter estimation is investigated. The objective is to find a quantiser to minimise the variance of the estimation error by the minimum mean-square estimation. The main results are presented for the cases of high and low resolutions, respectively. For high resolution, multi-dimensional quantisation is considered and a quantitative relationship between the quantisation density and the probability density function is presented. For low-resolution case, an indirect method is developed for one-dimensional optimal quantisation by exploiting the results of high resolution case. The measurement space is first evenly divided into a number of small intervals, then the quantisation is approximately represented by the grouping of the small intervals. At last, a dynamic programming-based method is presented for the optimal grouping.
Starting Page	2195
Ending Page	2201
Page Count	7
ISSN	17518644
Volume Number	9
e-ISSN	17518652
Issue Number	Issue 15, Oct (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cta/9/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2015.0206
Journal	IET Control Theory & Applications
Publisher Date	2015-06-23
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Dynamic Programming Dynamic Programming-based Method Interpolation And Function Approximation Least Mean Squares Method Minimum Mean-square Estimation Modelling And Identification Multidimensional Quantisation Numerical Analysis Optimal Quantisation Optimal Quantiser Design Optimisation Technique Parameter Estimation Probability Probability Density Function Random Parameter Estimation Simulation Statistics
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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