Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (9): 2712-2716.DOI: 10.11772/j.issn.1001-9081.2017.09.2712

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Air target threat assessment based on improved ACPSO algorithm and LSSVM

XU Lingkai, YANG Rennong, ZUO Jialiang   

  1. College of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an Shaanxi 710038, China
  • Received:2017-04-06 Revised:2017-06-07 Online:2017-09-10 Published:2017-09-13
  • Supported by:
    This work is partially supported by the National Science Fund for Distinguished Young Scholars (71501184).


许凌凯, 杨任农, 左家亮   

  1. 空军工程大学 航空航天工程学院, 西安 710038
  • 通讯作者: 许凌凯,
  • 作者简介:许凌凯(1993-),男,湖北鄂州人,硕士研究生,主要研究方向:机器学习与智能空战;杨任农(1968-),男,四川彭州人,教授,博士,主要研究方向:航空兵任务规划与作战效能评估;左家亮(1987-),男,陕西西安人,博士,主要研究方向:航空兵任务规划与作战效能评估。
  • 基金资助:

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.

Key words: threat assessment, air defense operation, Adaptive Crossbreeding Particle Swarm Optimization (ACPSO), Least Squares Support Vector Machine (LSSVM)

摘要: 评估空中目标威胁程度是防空指挥控制系统的核心环节,评估的准确程度将对防空作战产生重大影响。针对传统评估方法实时性差、工作量大、评估精度不足、无法同时进行多目标评估等缺陷,提出了一种基于自适应杂交粒子群优化(ACPSO)算法和最小二乘支持向量机(LSSVM)的空中目标威胁评估方法。首先,根据空中目标态势信息构建威胁评估系统框架;然后,采用ACPSO算法对LSSVM中的正则化参数和核函数参数进行寻优,针对传统杂交机制的不足提出改进的交叉杂交方式,并使杂交概率自适应调整;最后,对比分析了各系统的训练和评估效果,并用优化后的系统实现多目标实时动态威胁评估。仿真结果表明,所提方法评估精度高,所需时间短,可同时进行多目标评估,为空中目标威胁评估提供了一种有效的解决方法。

关键词: 威胁评估, 防空作战, 自适应杂交粒子群优化, 最小二乘支持向量机

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