Open AccessOptimal quantisation for random parameter estimation
Author(s) -
Wu Hao
Publication year - 2015
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2015.0206
Subject(s) - estimation theory , control theory (sociology) , computer science , mathematics , mathematical optimization , algorithm , artificial intelligence , control (management)
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.