关键词: 最小二乘支持向量机, 自适应杂交粒子群, 威胁评估, 径向基, 专家系统

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, lack of evaluation accuracy, and can not 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) is proposed. Firstly, according to the air target situation information, the framework of threat assessment system is constructed. Then, ACPSO algorithm is 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 is proposed, and the crossbreeding probability is adjusted adaptively. Finally, the training and evaluation results of the systems are compared and analyzed, and the multi-target real-time dynamic threat assessment is realized by the optimized system. Simulation results show that the proposed method has the advantage of high accuracy and short time required, can be used to evaluate multiple targets simultaneously, it provides an effective solution to evaluate the threat of air targets.

Key words: least squares support vector machine, adaptive crossbreeding particle swarm optimization, threat assessment, radial basis function, expert system