Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1628001(2021)
Threat Assessment Method for UAV Based on a Bayesian Network with a Small Dataset
Fig. 1. Relationship between data volume and error
Fig. 2. Battlefield scenario of UAV threat assessment
Fig. 3. UAV threat assessment framework diagram
Fig. 4. BN structure matrix expression
Fig. 5. Construction flow chart of the constraint matrix
Fig. 6. Flow chart of BN structure learning algorithm based on matrix constraints
Fig. 7. Flow chart of parameter learning algorithm based on prior constraints
Fig. 8. Total BIC score of the algorithm proposed in this paper and the K2 algorithm
Fig. 9. Average BIC score of the algorithm proposed in this paper and K2 algorithm
Fig. 10. Total 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
Fig. 12. Threat assessment network based on 20 sets of data by our algorithm
Fig. 13. Threat assessment network based on 30 sets of data by our algorithm
Fig. 14. Threat assessment network based on 40 sets of data by our algorithm
Fig. 15. Threat assessment network based on 50 sets of data by our algorithm
Fig. 16. UAV threat assessment network model structure
Fig. 17. Threat assessment network derived from 40 sets of data by K2 algorithm
Fig. 18. Threat assessment network derived from 50 sets of data by K2 algorithm
Fig. 19. Threat assessment network derived from 60 sets of data by K2 algorithm
Fig. 20. Threat assessment network derived from 70 sets of data by K2 algorithm
Fig. 21. Relationship between data volume and threat probability
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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
Category: Remote Sensing and Sensors
Received: Sep. 23, 2020
Accepted: Dec. 8, 2020
Published Online: Aug. 20, 2021
The Author Email: Di Ruohai (xfwtdrh@163.com)