Abstract
In order to predict the air combat data accurately and quickly, a prediction method is developed for the aerial target based on autoregressive integrated moving average (ARIMA) model in this paper. The air combat situation data mainly consists of the velocity, altitude of aerial target and the angle between the target line of sight and target velocity. Finally, with an example, the simulation results indicate that the developed method can accurately and efficiently predict the air combat state data.
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References
A. Pongpunwattana, R. Rysdyk, Real-time planning for multiple autonomous vehicles in dynamic uncertain environments. J. Aerosp. Comput. Inf. Commun. 12(1), 580–604 (2004)
L. Sun, L. Yu, W. Huang, Improved weighted D-S evidence theory in the application of target intention prediction. J. Air Force Eng. Univ.: Nat. Sci. 10(1), 17–22 (2009)
Z. Yuan, The air target sequential game combat intention prediction model. Syst. Eng. Theor. Pract. 7(7), 70–76 (1997)
Y. Song, X. Zhang, Z. Wang, Target Intention Inference Model Based on Variable Structure Bayesian Network. Comput. Intell. Softw. Eng. pp. 1–4 (2009)
Y. Cui, Q. Wu, M. Chen, Aerial target intention prediction based on adaptive neuro-fuzzy inference system, in The 15th Chinese Conference on System Simulation Technology and Application, pp. 277–281 (2014)
T. Zhou, M. Chen, S. Chen, J. Zou, Intention prediction of aerial target under incomplete information. ICIC Express Lett. 8(3), 623–631 (2017)
S. Makridakis, M. Hibon, ARIMA models and the Box-Jenkins methodology. Appl. Econ. pp. 265–286 (2011)
P. Wang, H. Zhang, Z. Qin, G. Zhang, A novel hybrid-Garch model based on ARIMA and SVM for PM2.5 concentrations forecasting. Atmos. Pollut. Res. 1(8), 850–860 (2017)
Y. Yang, A study of prediction based on ARIMA model of road accidents. Stat. Appl. 2(6), 268–275 (2017)
J. Liu, H. Li, Price estimation of Pseudo-Ginseng based on ARIMA model and BP neural network. Comput. Sci. Appl. 7(7), 696–710 (2017)
K. Liang, Q. Pan, G. Song, X. Zhang, Z. Zhang, The study of multi-sensor time registration method. J. Shaanxi Univ. Sci. Technol. 24(6), 111–114 (2006)
Z. Pan, W. Dong, Z. Wang, D. Zhao, Study on PRS/IRS time registration based on curve fitting. J. Air Force Radar Acad. 25(5), 343–346 (2011)
Z.H. Munim, H. Schramm, Forecasting container shipping freight rates for the Far East C Northern Europe trade lane. Marit. Econ. Logistics 19(1), 106–125 (2017)
H. Akaike, A new look at the statistical model identification. IEEE Trans. Autom. Control 19(6), 716–723 (1974)
Acknowledgements
This work is partially supported by Equipment Pre-research Foundation of Laboratory (No. 61425040104) and Science and Technology on Electron-Optic Control Laboratory. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Zhou, T., Wu, Q., Chen, M. (2019). State Prediction Based on ARIMA Model for Aerial Target. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 529. Springer, Singapore. https://doi.org/10.1007/978-981-13-2291-4_33
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DOI: https://doi.org/10.1007/978-981-13-2291-4_33
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