A Novel Bayesian Filtering Method for Systems with Quantized Output Data

被引：3
|
作者：
Albornoz, Ricardo [1 ]
Carvajal, Rodrigo [1 ]
Aguero, Juan C. [1 ]
机构：
[1] Univ Tecn Federico Santa Maria, Dept Elect, Valparaiso, Chile
来源：
2019 IEEE CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (CHILECON) | 2019年
关键词：
State estimation; Quantized data; Gaussian sum approximation; IDENTIFICATION;
D O I：
10.1109/chilecon47746.2019.8987643
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper we develop a novel scheme for state estimation of discrete-time linear time-invariant systems with quantized output data. We take a Bayesian approach, therefore, we describe the behavior of the a posteriori probability density function of the state. The difficulty of this problem lies in the probability function of the measurable output given the state, which we approach through an approximation by a Gaussian sum, that naturally leads to a Gaussian sum for the a posteriori density function.
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收藏
页数：7
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