Consensus‐based robust least‐squares filter for multi‐sensor systems
Summary In this article, a consensus‐based robust regularized least‐squares filter is designed for multi‐sensor systems with norm‐bounded uncertainties. In this approach, a min‐max optimization problem is presented based on a consensus protocol on estimates. The advantage of a consensus filter is th...
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| Published in | International journal of adaptive control and signal processing Vol. 36; no. 5; pp. 1098 - 1115 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Wiley Subscription Services, Inc
01.05.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0890-6327 1099-1115 |
| DOI | 10.1002/acs.3385 |
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| Abstract | Summary
In this article, a consensus‐based robust regularized least‐squares filter is designed for multi‐sensor systems with norm‐bounded uncertainties. In this approach, a min‐max optimization problem is presented based on a consensus protocol on estimates. The advantage of a consensus filter is that each node estimates its local states, in addition to reaching an agreement on estimates made by all sensors on the network. By introducing appropriate conversions the proposed optimization problem is converted to a robust regularized least‐squares problem. Solving this problem results in the structure of the proposed filter. Then, the recursive formulation of the filter is obtained in measurement form and information form. Finally, in order to investigate the efficacy, good proficiency, and robustness of the proposed consensus‐based robust least‐squares filter, it has been applied to an uncertain multi‐sensor system with 100 nodes and its results have been compared with existing consensus filters. |
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| AbstractList | Summary
In this article, a consensus‐based robust regularized least‐squares filter is designed for multi‐sensor systems with norm‐bounded uncertainties. In this approach, a min‐max optimization problem is presented based on a consensus protocol on estimates. The advantage of a consensus filter is that each node estimates its local states, in addition to reaching an agreement on estimates made by all sensors on the network. By introducing appropriate conversions the proposed optimization problem is converted to a robust regularized least‐squares problem. Solving this problem results in the structure of the proposed filter. Then, the recursive formulation of the filter is obtained in measurement form and information form. Finally, in order to investigate the efficacy, good proficiency, and robustness of the proposed consensus‐based robust least‐squares filter, it has been applied to an uncertain multi‐sensor system with 100 nodes and its results have been compared with existing consensus filters. In this article, a consensus‐based robust regularized least‐squares filter is designed for multi‐sensor systems with norm‐bounded uncertainties. In this approach, a min‐max optimization problem is presented based on a consensus protocol on estimates. The advantage of a consensus filter is that each node estimates its local states, in addition to reaching an agreement on estimates made by all sensors on the network. By introducing appropriate conversions the proposed optimization problem is converted to a robust regularized least‐squares problem. Solving this problem results in the structure of the proposed filter. Then, the recursive formulation of the filter is obtained in measurement form and information form. Finally, in order to investigate the efficacy, good proficiency, and robustness of the proposed consensus‐based robust least‐squares filter, it has been applied to an uncertain multi‐sensor system with 100 nodes and its results have been compared with existing consensus filters. |
| Author | Rahmani, Mehdi Amiri Roshan, Soheila |
| Author_xml | – sequence: 1 givenname: Soheila surname: Amiri Roshan fullname: Amiri Roshan, Soheila organization: Imam‐Khomeini International University – sequence: 2 givenname: Mehdi orcidid: 0000-0002-4560-9018 surname: Rahmani fullname: Rahmani, Mehdi email: mrahmani@eng.ikiu.ac.ir |
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In this article, a consensus‐based robust regularized least‐squares filter is designed for multi‐sensor systems with norm‐bounded uncertainties. In... In this article, a consensus‐based robust regularized least‐squares filter is designed for multi‐sensor systems with norm‐bounded uncertainties. In this... |
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| Title | Consensus‐based robust least‐squares filter for multi‐sensor systems |
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