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
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摘要
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.
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.
引文
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[12] AJITANSHU V, GYANENDRA S, ANKIT K. Optimizing submerged arc welding using response surface methodology, regression analysis, and genetic algorithm[J]. Defence Technology,2018, 14(3):204-212.
[13]赵延龙,滑楠,于振华.基于二次搜索的改进粒子群算法[J].计算机应用, 2017, 37(9):2541-2546.(ZHAO Y L, HUA N,YU Z H. Improved particle swarm optimization algorithm based on twice search[J]. Journal of Computer Applications, 2017, 37(9):2541-2546.)
[2] BOX G P E, JENKINS G M, REINSEL G C. Time Series Analysis:Forecasting and Control[M]. London:Palgrave Macmillan, 2005:10.
[3] BUHMANN M D. Radial Basis Functions:Theory and Implementation[M]. New York:Cambridge University Press, 2003:56-104.
[4] CHEN R, TSAY R S. Functional-coefficient autoregressive models[J]. Journal of American Statistical Association, 1993, 88(421):298-308.
[5]吕永乐.一种新的统计预测模型——多项式系数自回归模型[J].计算机工程与应用, 2012, 48(3):237-241.(LYU Y L.New statistical prediction model:polynomial coefficient autoregressive model[J]. Computer Engineering and Applications, 2012, 48(3):237-241.)
[6] ZHAO J Z, DENG J Q, YE W, et al. Combined forecast method of HMM and LS-SVM about electronic equipment state based on MAGA[J]. Journal of Systems Engineering and Electronics, 2016, 27(3):730-738.
[7] MICOL F, THANH H L, RAF V. A comparison between the complex symmetric based and classical computation of the singular value decomposition of normal matrices[J]. Numerical Algorithms, 2014,67(1):109-120.
[8]李玉,张英海,赵雪梅,等.结合BIC准则和ECM算法的可变类SAR影像分割[J].中国矿业大学学报, 2017, 46(6):1402-1410.(LI Y, ZHANG Y H, ZHAO X M, et al. SAR image segmentation with variable class by combining BIC criterion and ECM algorithm[J]. Journal of China University of Mining&Technology,2017, 6(46):1402-1410.)
[9]李子达,廖士中.小样本贝叶斯网络参数学习方法[J].计算机工程, 2016, 42(8):153-159, 165.(LI Z D, LIAO S Z. Bayesian network parameter learning method on small samples[J]. Computer Engineering, 2016, 42(8):153-159, 165.)
[10] WANG T, WANG D, HU B, et al. Research on multiple elimination methods in inverse data space based on randomized singular value decomposition[J]. Global Geology, 2017, 20(1):59-63.
[11] HASSAN N, ESMAEIL N S, ABBAS S. Common weights in DEA models with reduced singular value decomposition[J]. Journal of Data Science, 2018, 2(16):419-430.
[12] AJITANSHU V, GYANENDRA S, ANKIT K. Optimizing submerged arc welding using response surface methodology, regression analysis, and genetic algorithm[J]. Defence Technology,2018, 14(3):204-212.
[13]赵延龙,滑楠,于振华.基于二次搜索的改进粒子群算法[J].计算机应用, 2017, 37(9):2541-2546.(ZHAO Y L, HUA N,YU Z H. Improved particle swarm optimization algorithm based on twice search[J]. Journal of Computer Applications, 2017, 37(9):2541-2546.)