Approximate Maximum-likelihood Identification of Linear Systems from Quantized Measurements

被引：4

作者：

Risuleo, Riccardo Sven ^{[1
]}

Bottegal, Giulio ^{[2
]}

Hjalmarsson, Hakan ^{[1
]}

机构：

[1] KTH Royal Inst Technol, ACCESS Linnaeus Ctr, Stockholm, Sweden

[2] Eindhoven Univ Technol, Dept Elect Engn, Eindhoven, Netherlands

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 15期

基金：

欧洲研究理事会; 瑞典研究理事会;

关键词：

Least-squares approximation; Maximum-likelihood estimators; quantized signals; BINARY-VALUED OBSERVATIONS; FIR SYSTEMS; OUTPUT DATA;

D O I：

10.1016/j.ifacol.2018.09.169

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We analyze likelihood-based identification of systems that are linear in the parameters from quantized output data; in particular, we propose a method to find approximate maximum-likelihood and maximum-a-posteriori solutions. The method consists of appropriate least-squares projections of the middle point of the active quantization intervals. We show that this approximation maximizes a variational approximation of the likelihood and we provide an upper bound for the approximation error. In a simulation study, we compare the proposed method with the true maximum-likelihood estimate of a finite impulse response model. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：724 / 729

页数：6

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