Abstract：The key link of air defense command and control system is to evaluate the threat degree of air target according to target situation information, the accuracy of the assessment will have a significant impact on air defense operations. Aiming at the shortcomings of traditional evaluation methods, such as poor real-time performance, heavy workload, low evaluation accuracy, and unable to evaluate multiple objectives simultaneously, a method of air target threat assessment based on Adaptive Crossbreeding Particle Swarm Optimization (ACPSO) and Least Squares Support Vector Machine (LSSVM) was proposed. Firstly, according to the air target situation information, the framework of threat assessment system was constructed. Then, ACPSO algorithm was used to optimize the regularization parameter and kernel function parameter in LSSVM. In order to overcome the disadvantages of the traditional crossbreeding mechanism, an improved cross-hybridization mechanism was proposed, and the crossbreeding probability was adjusted adaptively. Finally, the training and evaluation results of the systems were compared and analyzed, and the multi-target real-time dynamic threat assessment was realized by the optimized system. Simulation results show that the proposed method has the advantages of high accuracy and short time required, and can be used to evaluate multiple targets simultaneously. It provides an effective solution to evaluate the threat of air targets.
[1] MORELLAS V, PAVLIDIS I, TSIAMYRTZIS P. DETER:detection of evens for threat evaluation and recognition[J]. Machine Vision and Applications, 2003, 15(1):29-45. [2] 张旭东,彭杰,纪军.基于熵的逼近于理想解的排序法空袭目标威胁度评估[J].计算机应用,2011,31(11):3140-3142.(ZHANG X D, PENG J, JI J. Air-raid target threat degree evaluation based on entropy TOPSIS method[J]. Journal of Computer Applications, 2011, 31(11):3140-3142.) [3] 张肃.空中目标威胁评估技术[J].情报指挥控制系统与仿真技术,2005,27(1):41-45.(ZHANG S. Evaluation technology of the aerial targets' threat[J]. Information Command Control System & Simulation Technology, 2005, 27(1):41-45.) [4] 马其东,方立恭.海上区域防空目标威胁评估模型[J].现代防御技术,2009,37(1):15-19.(MA Q D, FANG L G. Research on threat assessment model of air target for area air defense[J]. Modern Defence Technology, 2009, 37(1):15-19.) [5] 胡起伟,石全,王广彦,等.基于神经网络的威胁评估研究[J].计算机仿真,2006,23(6):25-27.(HU Q W, SHI Q, WANG G Y, et al. Threat assessment based on neural network[J]. Computer Simulation, 2006, 23(6):25-27.) [6] 张发强,由大德,蒋敏.基于RBF神经网络的空袭目标威胁评估模型研究[J].舰船电子对抗,2010,33(6):85-88.(ZHANG F Q, YOU D D, JIANG M. Study of the threat assessment model for air raid targets based on RBF neural network[J]. Shipboard Electronic Countermeasure, 2010, 33(6):85-88.) [7] 郭辉,徐浩军,刘凌. 基于回归型支持向量机的空战目标威胁评估[J].北京航空航天大学学报,2010,36(1):123-126.(GUO H, XU H J, LIU L. Target threat assessment of air combat based on support vector machines for regression[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(1):123-126.) [8] 郭辉,吕英军,王平,等.基于区间支持向量回归的空战目标威胁评估[J].火力与指挥控制,2014,39(8):17-21.(GUO H, LYU Y J, WANG P, et al. Target threat assessment of air combat based on intervals and SVR[J]. Fire Control & Command Control, 2014, 39(8):17-21.) [9] 谷文成,柴宝仁,滕艳平.基于粒子群优化算法的支持向量机研究[J].北京理工大学学报,2014,34(7):705-709.(GU W C, CHAI B R, TENG Y P. Research on support vector machine based on particle swarm optimization[J]. Transactions of Beijing Institute of Technology, 2014, 34(7):705-709.) [10] BANSAL J C, SINGH P K, SARASWAT M, et al. Inertia weight strategies in particle swarm optimization[C]//Proceedings of the 20113rd World Congress on Nature and Biologically Inspired Computing. Piscataway, NJ:IEEE, 2011:633-469. [11] 王江荣,文晖,任泰明.基于杂交粒子群算法的多元线性回归参数估计及预测区间研究[J].水泥工程,2014(5):6-9.(WANG J R, WEN H, REN T M. Multiple linear regression parameters estimation and prediction intervals research based on hybrid particle swarm optimization[J]. Cement Engineering, 2014(5):6-9.) [12] SUYKENS J A K, LUKAS L, VANDEWALLE J. Sparse least squares support vecter machines for adaptive communication channel equalization[J]. International Journal of Applied Science and Engineering, 2005, 11(3):51-59. [13] FENG Y, TENG G F, WANG A X, et al. Chaotic inertia weight in particle swarm optimization[C]//Proceedings of the 20072nd International Conference on Innovative Computing, Information and Control. Piscataway, NJ:IEEE, 2007:475-476. [14] 李蓉,沈云波,刘坚.改进的自适应粒子群优化算法[J].计算机工程与应用,2015,51(13):31-36.(LI R, SHEN Y B, LIU J. Improved adaptive particle swarm optimization algorithm[J]. Computer Engineering and Applications, 2015, 51(13):31-36.) [15] LØVBJERG M, RASMUSSEN T K, KRINK T. Hybrid particle swarm optimiser with breeding and subpopulations[C]//Proceedings of International Conference on Genetic and Evolutionary Computation Conference.San Francisco:Morgan Kaufmann,2001:101-106.
2017, Vol. 37 
