平台式导引头隔离度模型在线辨识方法

On-line identification method for models of platform seeker disturbance rejection rate

针对平台导引头隔离度模型在线辨识问题,提出一种改进卷积神经网络的导引头隔离度模型辨识方法,实现对不同干扰力矩以及天线罩误差等干扰参数影响下产生的隔离度模型高效辨识。首先,建立平台导引头模型,推导出隔离度传递函数,并搭建基于隔离度寄生回路的制导回路平台,获取弹体扰动下的视线角速率信息作为训练和测试数据。然后,利用卷积神经网络对视线角速率信号进行特征提取和特征降维。最后,经分类输出模型诊断结果。仿真结果表明,所提辨识方法对隔离度模型识别正确率能够达到99.7%,相较于传统方法提高了模型辨识准确率和快速在线辨识能力,具有较好的工程应用前景。

To solve the problem of online identification of platform seeker disturbance rejection rate models,an improved convolutional neural network based seeker disturbance rejection rate model identification method is proposed to realize efficient identification of the model generated by different torques and radome errors.Firstly,the seeker model of the platform is established,the disturbance rejection rate transfer function is derived,the guidance loop platform based on the parasitic loop is built,and the line of sight angle rate information under the disturbance of the missile body is obtained as the training and test data.Then,convolutional neural network is used to extract and reduce the feature dimension of the line of sight angle rate signal.Finally,the model diagnosis results are output by classification.The simulation results show that the proposed identification method of disturbance rejection rate model recognition accuracy can reach 99.7%,which improves the identification accuracy and fast online identification ability compared with traditional methods,and has a good engineering application prospect.