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    Journals >Laser & Optoelectronics Progress >Volume 58 >Issue 16 >Page 1628001 > Article
    • Laser & Optoelectronics Progress
    • Vol. 58, Issue 16, 1628001 (2021)
    Threat Assessment Method for UAV Based on a Bayesian Network with a Small Dataset
    Ye Li1, Zhigang Lü1、2, Ruohai Di1、*, Liangliang Li1, Weiyao Zhang1, and Hongxi Wang2
    Author Affiliations
  • 1School of Electronic and Information Engineering, Xi'an Technological University, Xi'an, Shaaxi 710021, China
  • 2School of Mechatronic Engineering, Xi'an Technological University, Xi'an, Shaaxi 710021, China
    • show less
    DOI: 10.3788/LOP202158.1628001 Cite this Article Set citation alerts
    Ye Li, Zhigang Lü, Ruohai Di, Liangliang Li, Weiyao Zhang, Hongxi Wang. Threat Assessment Method for UAV Based on a Bayesian Network with a Small Dataset[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1628001 Copy Citation Text show less
    Relationship between data volume and error
    Fig. 1. Relationship between data volume and error
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    Battlefield scenario of UAV threat assessment
    Fig. 2. Battlefield scenario of UAV threat assessment
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    UAV threat assessment framework diagram
    Fig. 3. UAV threat assessment framework diagram
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    BN structure matrix expression
    Fig. 4. BN structure matrix expression
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    Construction flow chart of the constraint matrix
    Fig. 5. Construction flow chart of the constraint matrix
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    Flow chart of BN structure learning algorithm based on matrix constraints
    Fig. 6. Flow chart of BN structure learning algorithm based on matrix constraints
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    Flow chart of parameter learning algorithm based on prior constraints
    Fig. 7. Flow chart of parameter learning algorithm based on prior constraints
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    Total BIC score of the algorithm proposed in this paper and the K2 algorithm
    Fig. 8. Total BIC score of the algorithm proposed in this paper and the K2 algorithm
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    Average BIC score of the algorithm proposed in this paper and K2 algorithm
    Fig. 9. Average BIC score of the algorithm proposed in this paper and K2 algorithm
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    Total Hamming distance between the algorithm in this paper and the K2 algorithm
    Fig. 10. Total Hamming distance between the algorithm in this paper and the K2 algorithm
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    Average Hamming distance between the algorithm in this paper and the K2 algorithm
    Fig. 11. Average Hamming distance between the algorithm in this paper and the K2 algorithm
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    Threat assessment network based on 20 sets of data by our algorithm
    Fig. 12. Threat assessment network based on 20 sets of data by our algorithm
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    Threat assessment network based on 30 sets of data by our algorithm
    Fig. 13. Threat assessment network based on 30 sets of data by our algorithm
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    Threat assessment network based on 40 sets of data by our algorithm
    Fig. 14. Threat assessment network based on 40 sets of data by our algorithm
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    Threat assessment network based on 50 sets of data by our algorithm
    Fig. 15. Threat assessment network based on 50 sets of data by our algorithm
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    UAV threat assessment network model structure
    Fig. 16. UAV threat assessment network model structure
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    Threat assessment network derived from 40 sets of data by K2 algorithm
    Fig. 17. Threat assessment network derived from 40 sets of data by K2 algorithm
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    Threat assessment network derived from 50 sets of data by K2 algorithm
    Fig. 18. Threat assessment network derived from 50 sets of data by K2 algorithm
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    Threat assessment network derived from 60 sets of data by K2 algorithm
    Fig. 19. Threat assessment network derived from 60 sets of data by K2 algorithm
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    Threat assessment network derived from 70 sets of data by K2 algorithm
    Fig. 20. Threat assessment network derived from 70 sets of data by K2 algorithm
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    Relationship between data volume and threat probability
    Fig. 21. Relationship between data volume and threat probability
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    Target node nameMeaningValue range and meaning
    Type information IWhich of the two types does the target belong to{1, 2}={I1: Warning radar, I2: Antiaircraft Artillery Position(AAP)}
    Confrontation information KCombat capability of target weapon{1, 2}={K1: yes, K2: no}
    Location information LWhether UAV is in the detection range of target attack and search{1, 2}={L1: no, L2: yes}
    Threat level information TThreat level of the UAV relative to the target at the current moment{1, 2}={T1: low, T2: high}
    Table 1. Discretization of target node information
    Target threat(T1: low,T2: high)Target type(I1: Warning radar,I2: AAP)Target confrontation (K1: yes,K2: no)Target location (L1: far,L2: near)
    T1(low)[0.3,0.7]([0.3,0.7], [0.3,0.7])([0.7,0.9], [0.1,0.3])([0.3,0.7], [0.3,0.7])
    T2(high)[0.3,0.7]([0.8,1], [0,0.2])([0.2,0.4], [0.6,0.8])([0.5,0.7], [0.3,0.5])
    Table 2. Interval constraints corresponding to parameters
    Target threat(T1: low,T2: high)Target type(I1: Warning radar,I2: AAP)Target confrontation (K1: yes,K2: no)Target location (L1: far,L2: near)
    T1(low)0.5(0.5,0.5)(0.8,0.2)(0.5,0.5)
    T2(high)0.5(0.9,0.1)(0.3,0.7)(0.6,0.4)
    Table 3. Initial parameters of threat assessment model
    Target threat(T1: low,T2: high)Target type(I1: Warning radar,I2: AAP)Target confrontation(K1: yes,K2: no)Target location (L1: far,L2: near)
    T1(low)0.5173(0.4841,0.5159)(0.7866,0.2134)(0.5086,0.4914)
    T2(high)0.4827(0.9008,0.0992)(0.2240,0.7760)(0.64050.3595)
    Table 4. 40 sets of data model parameters obtained by our algorithm
    Target threat(T1: low,T2: high)Target type(I1: Warning radar,I2: AAP)Target confrontation (K1: yes,K2: no)Target location (L1: far,L2: near)
    T1(low)0.4867(0.4391,0.5609)(0.8094,0.1906)(0.4454,0.5546)
    T2(high)0.5133(0.8851,0.1149)(0.2189,0.7811)(0.5343,0.4657)
    Table 5. 50 sets of data model parameters obtained from our algorithm
    Target threat(T1: low,T2: high)Target type(I1: Warning radar,I2: AAP)Target confrontation (K1: yes,K2: no)Target location (L1: far,L2: near)
    T1(low)0.4657(0.4583,0.5417)(0.8146,0.1854)(0.4484,0.5516)
    T2(high)0.5343(0.8866,0.1134)(0.2380,0.7620)(0.5069,0.4931)
    Table 6. 60 sets of data model parameters obtained by our algorithm
    Target threat(T1: low,T2: high)Target type(I1: Warning radar,I2: AAP)Target confrontation (K1: yes,K2: no)Target location (L1: far,L2: near)
    T1(low)0.4750(0.4737,0.5263)(0.6184,0.3452)(0.4474,0.5526)
    T2(high)0.5250(0.6548,0.3452)(0.3929,0.6071)(0.4643,0.5357)
    Table 7. Model parameters obtained from 60 sets of data by MLE algorithm
    Target threat(T1: low,T2: high)Target type(I1: Warning radar,I2: AAP)Target confrontation(K1:yes,K2:no)Target location(L1: far,L2: near)
    T1(low)0.5286(0.3784,0.6216)(0.8649,0.1351)(0.4595,0.5405)
    T2(high)0.4714(0.7879,0.2121)(0.1212,0.8788)(0.6061,0.3939)
    Table 8. Model parameters obtained from 70 sets of data by MLE algorithm
    Target threat(T1: low,T2: high)Target type(I1: Warning radar,I2: AAP)Target confrontation(K1: yes,K2: no)Target location(L1: far,L2: near)
    T1(low)0.5875(0.4255,0.5745)(0.8298,0.1702)(0.3830,0.6170)
    T2(high)0.4125(0.9394,0.0606)(0.1515,0.8485)(0.6061,0.3939)
    Table 9. Model parameters obtained from 80 sets of data by MLE algorithm
    40 groups50 groups60 groups
    0.01630.01610.0156
    Table 10. KL-divergence results of this algorithm
    60 groups70 groups80 groups
    0.01880.01600.0132
    Table 11. KL-divergence results of MLE algorithm
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    Ye Li, Zhigang Lü, Ruohai Di, Liangliang Li, Weiyao Zhang, Hongxi Wang. Threat Assessment Method for UAV Based on a Bayesian Network with a Small Dataset[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1628001
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