AEROSPACE SHANGHAI, Volume. 42, Issue 4, 158(2025)
DBN-TOPSIS-based Multi-space Non-cooperative Target Threat Assessment Method
[4] Z CHENG, J LU, H DING et al. A superposition assessment framework of multi-source traffic risks for mega-events using risk field model and time-series generative adversarial networks. IEEE Transactions on Intelligent Transportation Systems, 24, 12736-12753(2023).
[5] H HUANG, J WANG, C FEI et al. A probabilistic risk assessment framework considering lane-changing behavior interaction. Science China Information Sciences, 63, 1-15(2020).
[6] Q ZHANG, J H HU, J FENG et al. Air multi-target threat assessment method based on improved GGIFSS. Journal of Intelligent & Fuzzy Systems, 36, 4127-4139(2019).
[7] E AZIMIRAD, J HADDADNIA. Target threat assessment using fuzzy sets theory. International Journal of Advances in Intelligent Informatics, 1, 57-74(2015).
[8] C FAN, Q FU, Y SONG et al. A new model of interval-valued intuitionistic fuzzy weighted operators and their application in dynamic fusion target threat assessment. Entropy, 24, 1825(2022).
[9] R ZHAO, F YANG, L JI. An extended fuzzy CPT-TODIM model based on possibility theory and its application to air target dynamic threat assessment. IEEE Access, 10, 21655-21669(2022).
[10] L YUE, R YANG, J ZUO et al. Air target threat assessment based on improved moth flame optimization-gray neural network model. Mathematical Problems in Engineering, 1-14(2019).
[11] N PALTRINIERI, L COMFORT, G RENIERS. Learning about risk:machine learning for risk assessment. Safety Science, 118, 475-486(2019).
[13] M KALANTARNIA, F KHAN, K HAWBOLDT. Dynamic risk assessment using failure assessment and Bayesian theory. Journal of Loss Prevention in the Process Industries, 22, 600-606(2009).
[14] Y DU, Q LIU, M CEN. A structure-variable bayesian network model for vehicle threat assessment, 1676(2020).
[15] G LI, X WU, J C HAN et al. Flood risk assessment by using an interpretative structural modeling-based Bayesian network approach (ISM-BN):An urban-level analysis of Shenzhen,China. Journal of Environmental Management, 329, 117040(2023).
[16] X WANG, J ZUO, R YANG et al. Target threat assessment based on dynamic Bayesian network, 1302(2019).
[17] Y BAI, J WU, Q REN et al. A BN-based risk assessment model of natural gas pipelines integrating knowledge graph and DEMATEL. Process Safety and Environmental Protection, 171, 640-654(2023).
[21] Y YIN, R ZHANG, Q SU. Threat assessment of aerial targets based on improved GRA-TOPSIS method and three-way decisions. Math.Biosci.Eng, 20, 13250-13266(2023).
[22] H CHAI, Y ZHANG, X LI et al. Aerial target threat assessment method based on deep learning. Journal of System Simulation, 34, 1459-1467(2022).
[24] J LI, L YUAN, C ZHANG et al. Fuzzy dynamic bayesian network based threat assessment model for space targets, 1176-1181(2022).
[29] C HE, J J LUO, Z YANG et al. Intention recognition for spacecraft formation based on two-layer temporal convolutional network-self attention. Aerospace Science and Technology, 158, 109939(2025).
[31] H ZHANG, J J LUO, Y GAO et al. An intention inference method for the space non-cooperative target based on BiGRU-Self Attention. Advances in Space Research, 72, 1815-1828(2023).
[35] G ZHAO, Y GUO, W DENG et al. Natural fly around orbital maneuvers strategy for geo spacecraft considering illumination constraints, 8182-8187(2019).
Get Citation
Copy Citation Text
Zhenqi YANG, Chang HE, Zhihang JING, Jianjun LUO. DBN-TOPSIS-based Multi-space Non-cooperative Target Threat Assessment Method[J]. AEROSPACE SHANGHAI, 2025, 42(4): 158
Category: Speciality Discussion
Received: Sep. 25, 2024
Accepted: --
Published Online: Sep. 29, 2025
The Author Email: