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Probabilistic Graph Optimization Integrated Navigation Based on Probabilistic Time Series Transformer
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Lei ZHANG1, 2, Qin XU2, Wanliang ZHAO3, Yuxiang CHENG3, Yan SUN2
Flight Control & Detection | 2025, 8(5) : 1 - 10
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Flight Control & Detection | 2025, 8(5): 1-10
Navigation, Guidance and Control Technology
Probabilistic Graph Optimization Integrated Navigation Based on Probabilistic Time Series Transformer
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Lei ZHANG1, 2, Qin XU2, Wanliang ZHAO3, Yuxiang CHENG3, Yan SUN2
Affiliations
  • 1Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201210
  • 2The Key Laboratory of Road and Traffic Engineering Ministry of Education, Tongji University, Shanghai 201804
  • 3Shanghai Aerospace Control Technology Institute, Shanghai 201109
doi: 10.20249/j.cnki.2096-5974.2025.05.001
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To satisfy the modern track traffic's demand for maintaining high precision and continuity of navigation under complex environmental conditions,and to address the issue of positioning drift caused by data outages in nonlinear dynamic integrated navigation systems,this paper proposes a novel adaptive estimation method for observation errors in nonlinear dynamic integrated navi gation systems. This method is based on a Probabilistic Time Series Transformer model,aiming to resolve the aforementioned issues. By introducing self-learning capabilities through the Probabilistic Time Series Transformer,the method adaptively adjusts the impact of state prediction and observation information outages on the dynamic navigation system. The Probabilistic Time Series Transformer is composed of a dual-loop system of a generative model and an inference model, combined with LSTM network to tackle the challenges of multivariate time series modeling. The integrated navigation system based on the Probabilistic Time Series Transformer optimizes the error compensation mechanism by establishing a relationship between the current Kalman filter gain and the optimal estimation error,thereby improving the accuracy and stability of the nonlinear navigation system. Experimental results demonstrate that the proposed method not only effectively controls the impact of GNSS outages on the nonlinear navigation system but also accurately estimates and compensates for observation model system errors. The average positioning error in various complex scenarios is less than 10m. The suppression of positioning drift in the observation model is better than that of other filtering methods.

integrated navigation  /  attention model  /  time series  /  track traffic  /  Kalman filtering
Lei ZHANG, Qin XU, Wanliang ZHAO, Yuxiang CHENG, Yan SUN. Probabilistic Graph Optimization Integrated Navigation Based on Probabilistic Time Series Transformer[J]. Flight Control & Detection, 2025 , 8 (5) : 1 -10 . DOI: 10.20249/j.cnki.2096-5974.2025.05.001
Year 2025 volume 8 Issue 5
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doi: 10.20249/j.cnki.2096-5974.2025.05.001
  • Online Date:2026-04-23
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Affiliations
    1Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201210
    2The Key Laboratory of Road and Traffic Engineering Ministry of Education, Tongji University, Shanghai 201804
    3Shanghai Aerospace Control Technology Institute, Shanghai 201109
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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