基于多项式系数自回归模型的雷达性能参数最优组合预测

Optimal combination prediction based on polynomial coefficient autoregressive model for radar performance parameter

针对雷达故障预测与健康管理(PHM)技术工程实现中性能参数变化趋势预测准确度不高的问题,提出一种基于多项式系数自回归(PCAR)模型的性能参数预测方法。首先,介绍了PCAR模型的形式及其阶次、参数确定方法,该模型相对于传统的线性模型扩大了模型选择范围,有效降低了建模偏差;然后,为了进一步提高预测准确度,采用基于奇异值分解滤波算法(SVDFA),选取最优门限值,将性能参数监测序列拆分成与各个失效因素对应的子序列,最后分别采用不同阶次的PCAR模型来预测序列未来值。仿真实验结果表明,所提出的联合PCAR模型的组合预测方法同单一自回归滑动平均模型(ARMA)的预测结果相比,三个监测序列的预测准确度分别提高了79.7%、97.6%和82.8%。实验结果表明该预测方法可应用于雷达性能参数的预测,有利于提高雷达的工作可靠性。

Aiming at low prediction accuracy of the variation trend of radar performance parameters in Prognostics and Health Management(PHM)of radar,a prediction method based on Polynomial Coefficient AutoRegressive(PCAR)model was proposed.Firstly,the form of PCAR model and methods of determining order and parameters were introduced.Compared with the traditional linear model,PCAR model expanded the model selection range and effectively reduced the modeling deviation.Then,to further improve prediction accuracy,the performance parameter monitoring sequence was divided into subsequences corresponding to each failure factor by selecting the optimal threshold on the basis of Singular Value Decomposition Filtering Algorithm(SVDFA).Finally,PCAR models with different orders were employed to realize the prediction.As shown in the simulation experiment,compared with the results predicted by the single AutoRegressive Moving Average model,the combined prediction method improves the accuracies of the three performance parameter monitoring sequences by 79.7%,97.6% and 82.8% respectively.The results show that the proposed method can be applied to the prediction of radar performance parameters and improve the operational reliability of radar.