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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/22/7675 |
| _version_ | 1797508454382829568 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:04:18Z |
| format | Article |
| id | doaj.art-96147113c8be49f388b358fe2989929c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:04:18Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-96147113c8be49f388b358fe2989929c2023-11-23T01:27:48ZengMDPI AGSensors1424-82202021-11-012122767510.3390/s21227675A Two-Filter Approach for State Estimation Utilizing Quantized Output DataAngel L. Cedeño0Ricardo Albornoz1Rodrigo Carvajal2Boris I. Godoy3Juan C. Agüero4Departamento Electrónica, Universidad Técnica Federico Santa María (UTFSM), Av. España 1680, Valparaíso 2390123, ChileDepartamento Electrónica, Universidad Técnica Federico Santa María (UTFSM), Av. España 1680, Valparaíso 2390123, ChileEscuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso 2374631, ChileDepartment of Mechanical Engineering, Boston University, Boston, MA 02215, USADepartamento Electrónica, Universidad Técnica Federico Santa María (UTFSM), Av. España 1680, Valparaíso 2390123, ChileFiltering 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.https://www.mdpi.com/1424-8220/21/22/7675state estimationquantized dataGaussian sum filteringGaussian sum smoothingGauss–Legendre quadrature |
| spellingShingle | Angel L. Cedeño Ricardo Albornoz Rodrigo Carvajal Boris I. Godoy Juan C. Agüero A Two-Filter Approach for State Estimation Utilizing Quantized Output Data Sensors state estimation quantized data Gaussian sum filtering Gaussian sum smoothing Gauss–Legendre quadrature |
| title | 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_short | A Two-Filter Approach for State Estimation Utilizing Quantized Output Data |
| title_sort | two filter approach for state estimation utilizing quantized output data |
| topic | state estimation quantized data Gaussian sum filtering Gaussian sum smoothing Gauss–Legendre quadrature |
| url | https://www.mdpi.com/1424-8220/21/22/7675 |
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