Optimal quantisation for random parameter estimation

Hao Wu

Optimal quantisation for random parameter estimation

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Author(s): Hao Wu ¹
- Affiliations: 1: Internet Society of China, CNCERT/CC, Beijing 100029, People's Republic of China
Source: Volume 9, Issue 15, 08 October 2015, p. 2195 – 2201
DOI: 10.1049/iet-cta.2015.0206 , Print ISSN 1751-8644, Online ISSN 1751-8652

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Received 09/03/2015, Accepted 31/05/2015, Revised 05/05/2015, Published 23/06/2015

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.

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