关键词: 雷达性能参数, 故障预测与健康管理, 多项式系数自回归模型, 序列分解, 最优组合预测, 基于奇异值分解滤波算法

Abstract: 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.

Key words: performance parameter of radar, Prognostics and Health Management (PHM), Polynomial Coefficient AutoRegressive (PCAR) model, sequence decomposition, optimal combination prediction, Singular Value Decomposition Filtering Algorithm (SVDFA)