A Two-Filter Approach for State Estimation Utilizing Quantized Output Data.
Sensors (Basel)
; 21(22)2021 Nov 18.
Article
em En
| MEDLINE
| ID: mdl-34833748
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.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Chile
País de publicação:
Suíça