Advances in Multi-Source Navigation Data Fusion Processing Methods
In recent years, the field of multi-source navigation data fusion has witnessed substantial advancements, propelled by the rapid development of multi-sensor technologies, Artificial Intelligence (AI) algorithms and enhanced computational capabilities. On one hand, fusion methods based on filtering t...
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MDPI AG
2025-04-01
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| author | Xiaping Ma Peimin Zhou Xiaoxing He |
| author_facet | Xiaping Ma Peimin Zhou Xiaoxing He |
| author_sort | Xiaping Ma |
| collection | DOAJ |
| description | In recent years, the field of multi-source navigation data fusion has witnessed substantial advancements, propelled by the rapid development of multi-sensor technologies, Artificial Intelligence (AI) algorithms and enhanced computational capabilities. On one hand, fusion methods based on filtering theory, such as Kalman Filtering (KF), Particle Filtering (PF), and Federated Filtering (FF), have been continuously optimized, enabling effective handling of non-linear and non-Gaussian noise issues. On the other hand, the introduction of AI technologies like deep learning and reinforcement learning has provided new solutions for multi-source data fusion, particularly enhancing adaptive capabilities in complex and dynamic environments. Additionally, methods based on Factor Graph Optimization (FGO) have also demonstrated advantages in multi-source data fusion, offering better handling of global consistency problems. In the future, with the widespread adoption of technologies such as 5G, the Internet of Things, and edge computing, multi-source navigation data fusion is expected to evolve towards real-time processing, intelligence, and distributed systems. So far, fusion methods mainly include optimal estimation methods, filtering methods, uncertain reasoning methods, Multiple Model Estimation (MME), AI, and so on. To analyze the performance of these methods and provide a reliable theoretical reference and basis for the design and development of a multi-source data fusion system, this paper summarizes the characteristics of these fusion methods and their corresponding application scenarios. These results can provide references for theoretical research, system development, and application in the fields of autonomous driving, unmanned vehicle navigation, and intelligent navigation. |
| format | Article |
| id | doaj-art-da7440f30d5f47dfa3b5d09998f24d11 |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-da7440f30d5f47dfa3b5d09998f24d112025-08-20T03:49:22ZengMDPI AGMathematics2227-73902025-04-01139148510.3390/math13091485Advances in Multi-Source Navigation Data Fusion Processing MethodsXiaping Ma0Peimin Zhou1Xiaoxing He2School of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Geomatics, Xi’an University of Science and Technology, Xi’an 710054, ChinaSchool of Civil Engineering and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaIn recent years, the field of multi-source navigation data fusion has witnessed substantial advancements, propelled by the rapid development of multi-sensor technologies, Artificial Intelligence (AI) algorithms and enhanced computational capabilities. On one hand, fusion methods based on filtering theory, such as Kalman Filtering (KF), Particle Filtering (PF), and Federated Filtering (FF), have been continuously optimized, enabling effective handling of non-linear and non-Gaussian noise issues. On the other hand, the introduction of AI technologies like deep learning and reinforcement learning has provided new solutions for multi-source data fusion, particularly enhancing adaptive capabilities in complex and dynamic environments. Additionally, methods based on Factor Graph Optimization (FGO) have also demonstrated advantages in multi-source data fusion, offering better handling of global consistency problems. In the future, with the widespread adoption of technologies such as 5G, the Internet of Things, and edge computing, multi-source navigation data fusion is expected to evolve towards real-time processing, intelligence, and distributed systems. So far, fusion methods mainly include optimal estimation methods, filtering methods, uncertain reasoning methods, Multiple Model Estimation (MME), AI, and so on. To analyze the performance of these methods and provide a reliable theoretical reference and basis for the design and development of a multi-source data fusion system, this paper summarizes the characteristics of these fusion methods and their corresponding application scenarios. These results can provide references for theoretical research, system development, and application in the fields of autonomous driving, unmanned vehicle navigation, and intelligent navigation.https://www.mdpi.com/2227-7390/13/9/1485multi-source navigationfusion processingLSEKFPFFG |
| spellingShingle | Xiaping Ma Peimin Zhou Xiaoxing He Advances in Multi-Source Navigation Data Fusion Processing Methods Mathematics multi-source navigation fusion processing LSE KF PF FG |
| title | Advances in Multi-Source Navigation Data Fusion Processing Methods |
| title_full | Advances in Multi-Source Navigation Data Fusion Processing Methods |
| title_fullStr | Advances in Multi-Source Navigation Data Fusion Processing Methods |
| title_full_unstemmed | Advances in Multi-Source Navigation Data Fusion Processing Methods |
| title_short | Advances in Multi-Source Navigation Data Fusion Processing Methods |
| title_sort | advances in multi source navigation data fusion processing methods |
| topic | multi-source navigation fusion processing LSE KF PF FG |
| url | https://www.mdpi.com/2227-7390/13/9/1485 |
| work_keys_str_mv | AT xiapingma advancesinmultisourcenavigationdatafusionprocessingmethods AT peiminzhou advancesinmultisourcenavigationdatafusionprocessingmethods AT xiaoxinghe advancesinmultisourcenavigationdatafusionprocessingmethods |