| Citation: | MENG G L，ZHANG H M，PIAO H Y，et al. Cooperative tactical recognition of dual-aircraft formation under incomplete information in BVR air combat[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（2）：284-294 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0251
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In the process of beyond-visual-range (BVR) air combat, due to the limitation of detection equipment performance and enemy interference, the target information is easy to get lost, which makes it difficult to identify the enemy’s cooperative air combat tactics in real time. A method of cooperative tactical recognition is proposed based on dynamic Bayesian network (DBN) and parameter learning. Firstly, the cooperative tactics of dual-aircraft formation in BVR air combat are analyzed. According to the functional tasks of leader and wingman, the current situation information and fighter maneuver, a DBN recognition model is established. Then, to improve the recognition rate of the model, the expected maximum parameter learning method is used to optimize the network parameters. Finally, based on the auto-regressive model, the missing target information is repaired, and the reasoning algorithm of cooperative tactical recognition under incomplete information is proposed. The simulation results show that the method of cooperative tactical recognition has high recognition accuracy and good real-time performance for cooperative tactics under incomplete information in BVR air combat.
| [1] |
MA Y, WANG G, HU X, et al. Cooperative occupancy decision making of multi-UAV in beyond-visual-range air combat: a game theory approach[J]. IEEE Access, 2020, 8: 11624-11634. doi: 10.1109/ACCESS.2019.2933022
|
| [2] |
HEEMIN S, JAEHYUN L, HYUNGI K, et al. An autonomous aerial combat framework for two-on-two engagements based on basic fighter maneuvers[J]. Aerospace Science and Technology, 2018, 72(2): 305-315.
|
| [3] |
邓海军, 尹全军, 胡记文, 等. 基于MEBN的战术意图识别[J]. 系统工程与电子技术, 2010, 32(11): 2374-2379.
DENG H J, YIN Q J, HU J W, et al. Tactical intention recognition based on multi-entity Bayesian network[J]. Systems Engineering and Electronics, 2010, 32(11): 2374-2379(in Chinese).
|
| [4] |
ZHAO J, YANG B. Graph model of group conflict decision making based on grey correlation[J]. Paper Asia, 2019, 2(2): 128-132.
|
| [5] |
LI Q, DIAO Y, GONG Z, et al. Grey language hesitant fuzzy group decision making method based on kernel and grey scale[J]. International Journal of Environmental Research and Public Health, 2018, 15(3): 436-450. doi: 10.3390/ijerph15030436
|
| [6] |
ZHOU T, CHEN M, WANG Y, et al. Information entropy-based intention prediction of aerial targets under uncertain and incomplete information[J]. Entropy, 2020, 22(3): 279-290. doi: 10.3390/e22030279
|
| [7] |
QINGXIAN W U, CHEN S, LIU Z, et al. Prediction of unmanned aerial vehicle target intention under incomplete information[J]. Scientia Sinica Informationis, 2020, 50(5): 704-717. doi: 10.1360/SSI-2019-0106
|
| [8] |
AHMED A A, MOHAMMED M F. SAIRF: A similarity approach for attack intention recognition using fuzzy min-max neural network[J]. Journal of Computational Science, 2018, 25(3): 467-473.
|
| [9] |
周旺旺, 姚佩阳, 张杰勇, 等. 基于深度神经网络的空中目标作战意图识别[J]. 航空学报, 2018, 39(11): 200-208. doi: 10.7527/S1000-6893.2018.22468
ZHOU W W, YAO P Y, ZHANG J Y, et al. Combat intention recognition for aerial targets based on deep neural network[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(11): 200-208(in Chinese). doi: 10.7527/S1000-6893.2018.22468
|
| [10] |
LI Y, HAN W, WANG Y. Deep reinforcement learning with application to air confrontation intelligent decision-making of manned/unmanned aerial vehicle cooperative system[J]. IEEE Access, 2020, 8: 67887-67898. doi: 10.1109/ACCESS.2020.2985576
|
| [11] |
欧微, 柳少军, 贺筱媛, 等. 基于时序特征编码的目标战术意图识别算法[J]. 指挥控制与仿真, 2016, 38(6): 36-41. doi: 10.3969/j.issn.1673-3819.2016.06.008
OU W, LIU S J, HE X Y, et al. Tactical intention recognition algorithm based on encoded temporal features[J]. Command Control & Simulation, 2016, 38(6): 36-41(in Chinese). doi: 10.3969/j.issn.1673-3819.2016.06.008
|
| [12] |
WANG H, WANG L, YU Q, et al. Online reliability prediction via motifs-based dynamic Bayesian networks for service-oriented systems[J]. IEEE Transactions on Software Engineering, 2017, 43(6): 556-579. doi: 10.1109/TSE.2016.2615615
|
| [13] |
CARLO C, FATEMWH V, PETER C. Bayesian network hybrid learning using an elite-guided genetic algorithm[J]. Artificial Intelligence Review, 2019, 52(1): 245-272. doi: 10.1007/s10462-018-9615-5
|
| [14] |
SIQI N, MENG Z, QIANG J. The deep regression Bayesian network and its applications: Probabilistic deep learning for computer vision[J]. IEEE Signal Processing Magazine, 2018, 35(1): 101-111. doi: 10.1109/MSP.2017.2763440
|
| [15] |
CHOI A, WANG R, DARWICHE A. On the relative expressiveness of Bayesian and neural networks[J]. International Journal of Approximate Reasoning, 2019, 113: 303-323. doi: 10.1016/j.ijar.2019.07.008
|
| [16] |
GRUBER A, BEN-GAL I. A targeted Bayesian network learning for classification[J]. Quality Technology & Quantitative Management, 2019, 16(3): 243-261.
|
| [17] |
AJMAL H, MADDEN M G. Dynamic Bayesian network learning to infer sparse models from time series gene expression Data[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021, 99(1): 1-12.
|
| [18] |
GEDUK S, ULUSOY L. A practical analysis of sample complexity for structure learning of discrete dynamic Bayesian networks[J]. Optimization, 2022, 71(10): 2935-2962. doi: 10.1080/02331934.2021.1892105
|
| [19] |
孟光磊, 张慧敏, 朴海音, 等. 自动化飞行训练评估中的战机机动动作识别[J]. 北京航空航天大学学报, 2020, 46(7): 1267-1274. doi: 10.13700/j.bh.1001-5965.2019.0445
MENG G L, ZHANG H M, PIAO H Y, et al. Recognition of fighter maneuver in automatic fight training evaluation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7): 1267-1274(in Chinese). doi: 10.13700/j.bh.1001-5965.2019.0445
|
| [20] |
HONDA A, JAMES S. Parameter learning and applications of the inclusion-exclusion integral for data fusion and analysis[J]. Information Fusion, 2020, 56: 28-38. doi: 10.1016/j.inffus.2019.10.004
|
| [21] |
KOVACIC J. Learning parameters of Bayesian networks from datasets with systematically missing data: A meta-analytic approach[J]. Expert Systems with Application, 2020, 141(1): 1-11.
|
| [22] |
WANG D, AMRILJAHARADAK A, XIAO Y. Dynamic knowledge inference based on Bayesian network learning[J]. Mathematical Problems in Engineering, 2020, 2020: 1-9.
|
| [23] |
HU J, QIN H, YAN T, et al. Corrected Bayesian information criterion for stochastic block models[J]. Journal of the American Statistical Association, 2019, 115(532): 1-43.
|
| [24] |
LIAO T F, FASANG A E. Comparing groups of life-course sequences using the Bayesian information criterion and the likelihood-ratio test[J]. Sociological Methodology, 2021, 51(1): 44-85. doi: 10.1177/0081175020959401
|
| [25] |
POIRION F. Monte Carlo approach for fatigue and damage calculation of nonlinear dynamical systems[J]. PAMM, 2015, 1(1): 460-461.
|
| [26] |
RAHRAH M, VERMOLEN F. Monte Carlo assessment of the impact of oscillatory and pulsating boundary conditions on the flow through porous media[J]. Transport in Porous Media, 2018, 123: 125-146. doi: 10.1007/s11242-018-1028-z
|
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