基于RCGA优化GRNN的实时动态目标追踪模型

RT dynamic tracking target model based on GRNN optimized by RCGA

针对三维环境中导弹追踪目标时制导和控制算法复杂而导致计算量非常大的问题,提出了一种基于隐性交叉遗传算法优化广义回归神经网络的实时动态目标追踪模型。通过将导弹防御区离散化为多个小模块生成输入数据,并针对每个可接受的目标参数数据集,使用RCGA估算导航常量和导弹注意时间;利用输入和输出的目标参数集生成GRNN所需的训练数据集;针对任意位置的目标轨道,将训练后的GRNN应用于实时导弹导引系统的实现中。通过战术目标仿真模型验证了所提算法的有效性及可靠性,仿真结果表明,相比其他几种目标追踪算法,算法取得了更好的实时性和更高的目标定位精度,脱靶率接近零。

Guidance and control algorithm is complex when missile tracking targets in three-dimensional environment, which will cause complex computing, for which a real-time dynamic tracking target model based on general regression neural network optimized by recessive crossover genetic algorithm is proposed. Missile defense is discretized into many little models so as to generating imputing data, RCGA is used to estimate the navigation constant and notice time for each acceptable target parameter data set. Inputted and outputted target parameter data sets are used to generate training set needed by GRNN. The trained GRNN is applied into implementation of missile guidance real-time system. The effectiveness and reliability of proposed algorithm has been verified by tactical target simulation model. Simulation results show that proposed algorithm has better instantaneity and higher target positional accuracy than several target tracking algorithms.