一种基于在线修正时间序列预测方法的UCAV粗决策模型

A Rough Decision-making Model for UCAV Based on On-line Correction Time Series Forecasting Method

针对机载传感器不能在作战时限内获取目标参数从而造成属性匹配过程失败的情况,提出了一种基于在线修正时间序列预测方法的UCAV粗决策模型。通过对以往时刻传感器数据的时间序列分析,建立最优自回归移动平均模型,根据延迟获取的数据,运用在线修正预测方法,完成对未来时刻传感器数据的预测及预测值的修正,作为粗糙集决策属性匹配的输入完成决策过程。通过对UCAV目标威胁估计实例的分析可知,该模型能在决策数据的基础上,根据时间序列预测值,提取出所有条件下的决策规则,给出有效决策建议。

To deal with the matter that airborne sensors may not acquire targets＂ parameters in combat time limitation, which leads to failure of attributes matching, a rough decision - making model for UCAV based on on - hne correction time series forecasting method is presented. With the time series analysis results of sensors＇data in previous time, the optimal autoregressive moving average model is constructed. With the acquired delayed data, sensors ＂data in future time, which would be used as an input to the process of attributes matching , are predicted and the data are corrected by on - line correction prediction method. As the analysis of an example of target threat assessment for UCAV shows, the decision - making model can extract rules in all conditions and present efficient decision making adviees.