On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data

The problem of state estimation of a linear, dynamical state-space system where the output is subject to quantization is challenging and important in different areas of research, such as control systems, communications, and power systems. There are a number of methods and algorithms to deal with thi...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) Vol. 11; no. 6; p. 1327
Main Authors: Cedeño, Angel L., González, Rodrigo A., Godoy, Boris I., Carvajal, Rodrigo, Agüero, Juan C.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2023
Subjects:
Accuracy
Algorithms
Approximation
Computational efficiency
Computing costs
Control Engineering
Cost analysis
Electrical Engineering, Electronic Engineering, Information Engineering
Elektroteknik och elektronik
Engineering and Technology
Extended Kalman filter
extended Kalman filter/smoother
Gaussian sum filter/smoother
Kalman filters
Mathematical research
Mathematics
Noise
particle filter/smoother
quantized data
Random variables
Reglerteknik
Sensors
Smoothing
Smoothing (Statistics)
State estimation
State space models
State-of-the-art reviews
Teknik
unscented Kalman filter/smoother
Germany
ISSN:2227-7390, 2227-7390
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The problem of state estimation of a linear, dynamical state-space system where the output is subject to quantization is challenging and important in different areas of research, such as control systems, communications, and power systems. There are a number of methods and algorithms to deal with this state estimation problem. However, there is no consensus in the control and estimation community on (1) which methods are more suitable for a particular application and why, and (2) how these methods compare in terms of accuracy, computational cost, and user friendliness. In this paper, we provide a comprehensive overview of the state-of-the-art algorithms to deal with state estimation subject to quantized measurements, and an exhaustive comparison among them. The comparison analysis is performed in terms of the accuracy of the state estimation, dimensionality issues, hyperparameter selection, user friendliness, and computational cost. We consider classical approaches and a new development in the literature to obtain the filtering and smoothing distributions of the state conditioned to quantized data. The classical approaches include the extended Kalman filter/smoother, the quantized Kalman filter/smoother, the unscented Kalman filter/smoother, and the sequential Monte Carlo sampling method, also called particle filter/smoother, with its most relevant variants. We also consider a new approach based on the Gaussian sum filter/smoother. Extensive numerical simulations—including a practical application—are presented in order to analyze the accuracy of the state estimation and the computational cost.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math11061327