A Two-Filter Approach for State Estimation Utilizing Quantized Output Data

Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the sys...

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Autores principales:	Angel L. Cedeño, Ricardo Albornoz, Rodrigo Carvajal, Boris I. Godoy, Juan C. Agüero
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	state estimation quantized data Gaussian sum filtering Gaussian sum smoothing Gauss–Legendre quadrature Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/96147113c8be49f388b358fe2989929c
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spelling	oai:doaj.org-article:96147113c8be49f388b358fe2989929c2021-11-25T18:58:23ZA Two-Filter Approach for State Estimation Utilizing Quantized Output Data10.3390/s212276751424-8220https://doaj.org/article/96147113c8be49f388b358fe2989929c2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7675https://doaj.org/toc/1424-8220Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitalization process for storing or transmitting the measurements through a communication channel using a minimum amount of resources. Thus, obtaining suitable state estimates in this context is essential. In this paper, Gaussian sum filtering and smoothing algorithms are developed in order to deal with noisy measurements that are also subject to quantization. In this approach, the probability mass function of the quantized output given the state is characterized by an integral equation. This integral was approximated by using a Gauss–Legendre quadrature; hence, a model with a Gaussian mixture structure was obtained. This model was used to develop filtering and smoothing algorithms. The benefits of this proposal, in terms of accuracy of the estimation and computational cost, are illustrated via numerical simulations.Angel L. CedeñoRicardo AlbornozRodrigo CarvajalBoris I. GodoyJuan C. AgüeroMDPI AGarticlestate estimationquantized dataGaussian sum filteringGaussian sum smoothingGauss–Legendre quadratureChemical technologyTP1-1185ENSensors, Vol 21, Iss 7675, p 7675 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	state estimation quantized data Gaussian sum filtering Gaussian sum smoothing Gauss–Legendre quadrature Chemical technology TP1-1185
spellingShingle	state estimation quantized data Gaussian sum filtering Gaussian sum smoothing Gauss–Legendre quadrature Chemical technology TP1-1185 Angel L. Cedeño Ricardo Albornoz Rodrigo Carvajal Boris I. Godoy Juan C. Agüero A Two-Filter Approach for State Estimation Utilizing Quantized Output Data
description	Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitalization process for storing or transmitting the measurements through a communication channel using a minimum amount of resources. Thus, obtaining suitable state estimates in this context is essential. In this paper, Gaussian sum filtering and smoothing algorithms are developed in order to deal with noisy measurements that are also subject to quantization. In this approach, the probability mass function of the quantized output given the state is characterized by an integral equation. This integral was approximated by using a Gauss–Legendre quadrature; hence, a model with a Gaussian mixture structure was obtained. This model was used to develop filtering and smoothing algorithms. The benefits of this proposal, in terms of accuracy of the estimation and computational cost, are illustrated via numerical simulations.
format	article
author	Angel L. Cedeño Ricardo Albornoz Rodrigo Carvajal Boris I. Godoy Juan C. Agüero
author_facet	Angel L. Cedeño Ricardo Albornoz Rodrigo Carvajal Boris I. Godoy Juan C. Agüero
author_sort	Angel L. Cedeño
title	A Two-Filter Approach for State Estimation Utilizing Quantized Output Data
title_short	A Two-Filter Approach for State Estimation Utilizing Quantized Output Data
title_full	A Two-Filter Approach for State Estimation Utilizing Quantized Output Data
title_fullStr	A Two-Filter Approach for State Estimation Utilizing Quantized Output Data
title_full_unstemmed	A Two-Filter Approach for State Estimation Utilizing Quantized Output Data
title_sort	two-filter approach for state estimation utilizing quantized output data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/96147113c8be49f388b358fe2989929c
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A Two-Filter Approach for State Estimation Utilizing Quantized Output Data

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