基于SABA优化的Volterra级数空战目标机动轨迹预测

目标机动轨迹预测是空战态势感知和目标威胁评估的重要前提。针对传统目标机动轨迹预测模型复杂度大、预测精度低等问题，通过分析并结合目标机动轨迹时序数据所具备的混沌特性，引入Volterra泛函级数模型进行目标机动轨迹预测。为解决Volterra泛函级数模型中存在高阶核函数难以求解的问题，利用变异机制和自适应步长控制机制改进蝙蝠算法的寻优能力，进而构建了一种基于自适应蝙蝠算法（SABA）优化的Volterra泛函级数目标机动轨迹预测模型，并利用优化后不同阶数的Volterra泛函级数模型对目标未来机动轨迹进行预测。仿真实验中，通过与其他优化算法改进的Volterra泛函级数模型的预测精度对比，验证了所提预测模型的可行性，同时也说明了二阶Volterra泛函级数模型更加适用于目标机动轨迹预测。 

Air combat maneuver trajectory prediction of target based on Volterra series optimized by SABA algorithm

Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and target threat assessment. Aiming at the problems of high complexity and low prediction accuracy of traditional target maneuvering trajectory prediction model, Volterra functional series model was introduced to predict the target maneuvering trajectory by analyzing and combining the chaotic characteristics of target maneuvering trajectory time series data. To solve the problem that it is difficult to solve the high-order kernel function in Volterra functional series model, the mutation mechanism and adaptive step control mechanism were used to improve the optimization ability of bat algorithm. Then, a Volterra functional series target maneuver trajectory prediction model based on self-adaptive bat algorithm (SABA) optimization was constructed, and the future maneuvering trajectory of the target was predicted by using the optimized Volterra series model with different orders. In the simulation experiment, the feasibility of the prediction model is verified by comparing with the prediction accuracy of the Volterra series prediction model improved by other optimization algorithms, and the second-order Volterra series model is proved to bemore suitable for target maneuver trajectory prediction.