方法对比
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| 传感器融合× | 数据融合× | |
|---|---|---|
| 领域 | 数据融合 | 数据融合 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2013 | 1997 |
| 提出者≠ | Khaleghi, Khamis, Karray & Razavi | David Hall & James Llinas |
| 类型≠ | Multi-source information integration pipeline | Multi-level information integration pipeline |
| 开创性文献≠ | Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28–44. DOI ↗ | Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6–23. DOI ↗ |
| 别名 | Multisensor Data Fusion, Multi-Sensor Integration, Information Fusion, Sensör Füzyonu | Sensor Data Fusion, Information Fusion, Multi-source Data Fusion, Veri Füzyonu |
| 相关 | 3 | 3 |
| 摘要≠ | Sensor fusion is a computational process that combines data from multiple heterogeneous sensors to produce an estimate of the environment that is more accurate, complete, and reliable than any single source alone. Systematized as a formal field by Khaleghi, Khamis, Karray, and Razavi in their 2013 state-of-the-art review in Information Fusion, the discipline addresses imperfections such as noise, incompleteness, temporal misalignment, and conflicting readings that arise whenever multiple sensing modalities operate in parallel. | Data fusion is a multi-level process that combines data and information from multiple sensors and sources to achieve improved accuracy, completeness, and confidence in estimates that cannot be obtained from any single source alone. Formally introduced as the Joint Directors of Laboratories (JDL) model by Hall and Llinas in 1997, the framework organizes fusion into hierarchical processing levels ranging from raw signal combination to higher-order situation and threat assessment. |
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