防空导弹IMU状态的时间序列预测方法

A Time Series Method for Anti-aircraft Missile IMU State Prediction

惯性测量单元(IMU)是防空导弹的重要部分,其状态数据主要从定期人工测试中获取,效率较低。为了降低对定期测试的依赖,本文通过时间序列预测方法,从已有数据中预测其未来一段时间内的状态。出于小样本考虑,本文使用重叠分段平均进行数据处理,降低数据维度与训练难度,并用长短时记忆网络(LSTM)进行时间维度的预测。本文提出模型在实测数据上进行了验证,在获得最高预测精度的同时保持较低开销。

Inertial measurement unit(IMU)is an important component of anti-aircraft missiles and its state is mainly attained from periodical manual testing,which is not effcient.Aiming at reducing the dependence on periodical testing,time series prediction methods are developed to predict part of the states of IMU by processing existing data.Under the consideration of the small sample size,overlapping segmentation averaging is used for data processing to reduce feature dimension and training dificulty.Long short term memory network(LSTM)is used for subsequent time series prediction.The effect of the proposed model is validated through practical data,which attains a high prediction accuracy while taking little time consumption.