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Factor Graph-Based Multi-Sensor Fusio Ambient-Intelligent Integrated Navigation Design

Author

Listed:
  • Xia Liao

    (School of Art and Design, Sias University, China)

  • Xinye Ma

    (School of Automation and Electrical Engineering, Lanzhou Jiaotong University, China & Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province, China)

Abstract

Multi-sensor integrated navigation is the core technology of ambient intelligence application. The traditional fusion algorithm has poor adaptability, low positioning accuracy, and insufficient robustness in complex environments. The factor graph has great potential in multi-sensor fusion, but it has not been fully applied in ambient intelligence navigation. This study analyzes the compatibility between the factor graph and ambient computing and proposes an ambient context-aware framework integrating dynamic noise estimation and sensor reliability evaluation. Experiments in urban canyons (satellite signal occlusion) and indoor warehouses (complex interference) show the proposed method reduces position error by 70.8%, 58.8%, and 53.3% compared with three traditional algorithms. The robustness improved to 18.8% compared to traditional methods, providing a high-precision, robust solution for ambient intelligence navigation.

Suggested Citation

  • Xia Liao & Xinye Ma, 2026. "Factor Graph-Based Multi-Sensor Fusio Ambient-Intelligent Integrated Navigation Design," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global Scientific Publishing, vol. 17(1), pages 1-17, January.
    • Handle: RePEc:igg:jaci00:v:17:y:2026:i:1:p:1-17
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