非平稳APMA模型自校正预报器及其应用

本文介绍了在具有时变均值和时变方差的随机干扰序列作用下,ARMA模型的在线辨识与自校正预报.用简单的ELS方法实现模型的在线辨识.对指数平滑预报器一步预报误差序列进行实时建模和实时修正的基础上,实现对未来值的自校正预报.应用于雷达测量飞机低空飞行数据的短期预报,仿真和对实测数据的处理结果表明,此方法简单且精度较高,适用于微型机对一类非平稳过程的实时建模和短期预报.

A Self-Tuning Predictor of Non-Stationary Arma Model and its Application

On-line identification and self-tuning prediction of the ARMA under the influenceof stochastic distribution sequence with time varying mean and time varying variancehave been studied. The on-line identification of the model can be achieved by a simpleELS method. The self-tuning prediction of the future value can be achieved on thebasis of real time model-building and modification to the one-step predicting errors sequence in the exponential smooth predictor.It can be used for short period prediction of low altitude flight data by means ofRader measuring. Simulation and processed results of the measuring data show that themethod is simple and can acquire high precision. It can be used for real time modelbuilding and short period prediction to some kind of non-stationary process via microcomputer.