Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment. To solve the problem of low prediction accuracy of the traditional prediction method and model, a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function (PSR-RBF) neural network is established by combining the characteristics of trajectory with time continuity. In order to further improve the prediction performance of the model, the rival penalized competitive learning (RPCL) algorithm is introduced to determine the structure of RBF, the Levenberg-Marquardt (LM) and the hybrid algorithm of the improved particle swarm optimization (IPSO) algorithm and the k-means are introduced to optimize the parameter of RBF, and a PSR-RBF neural network is constructed. An independent method of 3D coordinates of the target maneuver trajectory is proposed, and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument (ACMI), and the maneuver trajectory prediction model based on the PSR-RBF neural network is established. In order to verify the precision and real-time performance of the trajectory prediction model, the simulation experiment of target maneuver trajectory is performed. The results show that the prediction performance of the independent method is better, and the accuracy of the PSR-RBF prediction model proposed is better. The prediction confirms the effectiveness and applicability of the proposed method and model.

中文翻译：

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混合算法优化的基于RBF神经网络的目标机动轨迹预测

目标机动轨迹预测在空战态势感知和威胁评估中起着重要作用。为了解决传统预测方法和模型的预测精度低的问题，结合了轨迹的特征和时间连续性，建立了基于相空间重构径向基函数（PSR-RBF）神经网络的目标机动轨迹预测模型。为了进一步提高模型的预测性能，引入了竞争惩罚竞争学习（RPCL）算法来确定RBF，Levenberg-Marquardt（LM）和改进的粒子群优化算法（IPSO）的混合算法的结构。引入算法和k均值对RBF参数进行优化，构造了PSR-RBF神经网络。提出了一种独立的目标机动轨迹的3D坐标方法，并利用空战机动工具（ACMI）中选择的训练数据和基于PSR-的机动轨迹预测模型构造了目标机动轨迹样本数据。建立了RBF神经网络。为了验证轨迹预测模型的精度和实时性，进行了目标机动轨迹的仿真实验。结果表明，独立方法的预测性能较好，所提出的PSR-RBF预测模型的精度较高。该预测证实了所提出的方法和模型的有效性和适用性。利用空战机动工具（ACMI）中选择的训练数据构造目标机动轨迹样本数据，建立了基于PSR-RBF神经网络的机动轨迹预测模型。为了验证轨迹预测模型的精度和实时性，进行了目标机动轨迹的仿真实验。结果表明，独立方法的预测性能较好，所提出的PSR-RBF预测模型的精度较高。该预测证实了所提出的方法和模型的有效性和适用性。利用空战机动工具（ACMI）中选择的训练数据构造目标机动轨迹样本数据，建立了基于PSR-RBF神经网络的机动轨迹预测模型。为了验证轨迹预测模型的精度和实时性，进行了目标机动轨迹的仿真实验。结果表明，独立方法的预测性能较好，所提出的PSR-RBF预测模型的精度较高。该预测证实了所提出的方法和模型的有效性和适用性。为了验证轨迹预测模型的精度和实时性，进行了目标机动轨迹的仿真实验。结果表明，独立方法的预测性能较好，所提出的PSR-RBF预测模型的精度较高。该预测证实了所提出的方法和模型的有效性和适用性。为了验证轨迹预测模型的精度和实时性，进行了目标机动轨迹的仿真实验。结果表明，独立方法的预测性能较好，所提出的PSR-RBF预测模型的精度较高。该预测证实了所提出的方法和模型的有效性和适用性。