Application Research and Improvement of Weighted Information Fusion Algorithm and Kalman Filtering Fusion Algorithm in Multi-sensor Data Fusion Technology
In order to improve the accuracy and reliability of the collected data, the weighted information fusion algorithm and the Kalman filtering fusion algorithm in multi-sensor data fusion technology are researched and improved. Firstly, the definition of multi-sensor data fusion and the basic working pr...
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| Vydané v: | Sensing and imaging Ročník 24; číslo 1; s. 43 |
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| Hlavný autor: | |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
16.11.2023
Springer Nature B.V |
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| ISSN: | 1557-2072, 1557-2064, 1557-2072 |
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| Abstract | In order to improve the accuracy and reliability of the collected data, the weighted information fusion algorithm and the Kalman filtering fusion algorithm in multi-sensor data fusion technology are researched and improved. Firstly, the definition of multi-sensor data fusion and the basic working principle of multi-sensor data fusion are expounded. Structural models of multi-sensor data fusion are introduced, the existing problems of multi-sensor data fusion are analyzed, the development trend of data fusion is pointed out. Secondly, the multi-sensor data fusion algorithms are analyzed, the weighted information fusion fusion algorithm and Kalman filtering algorithm in multi-sensor data fusion technology are focused on. Aiming at the deficiency of the weighted information fusion algorithm, an information fusion algorithm combining the jackknife method and the adaptive weighted method is proposed, and the basic steps of the improved fusion algorithm are given. The algorithm makes full use of the observed values and the estimated values of each historical moment, Quenouille estimation on the estimated values is performed by constructing pseudo-values. On the basis of the traditional Kalman filtering algorithm, an improved filtering algorithm is proposed, and a new state estimation equation is derived, which both treats the field value to prevent the filtering divergence, and introduces the observed value at the next moment to the state estimate at the current moment. Finally, improved fusion algorithms are applied and simulated in intelligent monitoring systems. Application and simulation results show that improved fusion algorithms are effective and superior, they have high reliability and anti-interference performances, the accuracy of the data is greatly increased, and they play a positive role in promoting the wide application of multi-sensor data fusion technology. |
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| AbstractList | In order to improve the accuracy and reliability of the collected data, the weighted information fusion algorithm and the Kalman filtering fusion algorithm in multi-sensor data fusion technology are researched and improved. Firstly, the definition of multi-sensor data fusion and the basic working principle of multi-sensor data fusion are expounded. Structural models of multi-sensor data fusion are introduced, the existing problems of multi-sensor data fusion are analyzed, the development trend of data fusion is pointed out. Secondly, the multi-sensor data fusion algorithms are analyzed, the weighted information fusion fusion algorithm and Kalman filtering algorithm in multi-sensor data fusion technology are focused on. Aiming at the deficiency of the weighted information fusion algorithm, an information fusion algorithm combining the jackknife method and the adaptive weighted method is proposed, and the basic steps of the improved fusion algorithm are given. The algorithm makes full use of the observed values and the estimated values of each historical moment, Quenouille estimation on the estimated values is performed by constructing pseudo-values. On the basis of the traditional Kalman filtering algorithm, an improved filtering algorithm is proposed, and a new state estimation equation is derived, which both treats the field value to prevent the filtering divergence, and introduces the observed value at the next moment to the state estimate at the current moment. Finally, improved fusion algorithms are applied and simulated in intelligent monitoring systems. Application and simulation results show that improved fusion algorithms are effective and superior, they have high reliability and anti-interference performances, the accuracy of the data is greatly increased, and they play a positive role in promoting the wide application of multi-sensor data fusion technology. |
| ArticleNumber | 43 |
| Author | Liu, Qiuxia |
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| CitedBy_id | crossref_primary_10_1088_1742_6596_2990_1_012029 crossref_primary_10_3390_s24237480 crossref_primary_10_1016_j_isatra_2025_07_006 crossref_primary_10_1016_j_ress_2025_111376 crossref_primary_10_3390_s25082628 crossref_primary_10_1038_s41598_025_10072_4 |
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| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. |
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448_CR25 publication-title: Process Automation Instrumentation – volume: 22 start-page: 1695 issue: 6 year: 2021 ident: 448_CR16 publication-title: International Journal of Automotive Technology doi: 10.1007/S12239-021-0146-8 |
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| Snippet | In order to improve the accuracy and reliability of the collected data, the weighted information fusion algorithm and the Kalman filtering fusion algorithm in... |
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| SubjectTerms | Accuracy Algorithms Credibility Data collection Data integration Decision making Divergence Electrical Engineering Engineering Imaging Information processing Information sources Kalman filters Microwaves Multisensor fusion Radiology Reliability RF and Optical Engineering Sensors State estimation Structural models |
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| Title | Application Research and Improvement of Weighted Information Fusion Algorithm and Kalman Filtering Fusion Algorithm in Multi-sensor Data Fusion Technology |
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