摘要
空中目标雷达探测距离的预测是基于检飞数据的雷达网探测能力分析中一个非常关键的环节.对小样本、贫信息的不确定性系统,采用一般预测方法很难满足预测精度的要求,为此使用灰色预测模型中的新陈代谢模型和Verhulst模型进行预测.新陈代谢模型在预测过程中不断剔除已不能反映系统特征的老数据,Verhulst模型则主要对原始数据变化呈S形特点的序列进行预测.最后通过实例对这两种模型进行了检验.检验结果表明:新陈代谢模型预测精度最低达96.14%,Verhulst模型预测精度最低达95.44%.
Prediction of radar detection range of air target is a key link in analysis of detection capability of radar network based on the flight test data.It is hard to meet the requirements for prediction precision by using a general prediction method for an uncertain system with small sample and lack of data and information.This paper attempts to use the metabolic one of grey prediction models and the Verhulst model for prediction,where the old data that can not reflect the system characteristics is continually rejected by the metabolic one while the Verhulst model is mainly used for predicting the sequence whose raw data change with the feature of ‘S’shape.Finally,the two models are verified by examples and the test results show that the forecast precision in metabolic model is at least 96.14% while that in the Verhulst model is 95.44%.
出处
《空军雷达学院学报》
2011年第1期6-8,24,共4页
Journal of Air Force Radar Academy
关键词
灰色预测
雷达探测距离
预测方法
grey prediction
radar detection range
prediction method
