基于奇异值分解的雷达性能组合预测方法

Combined prediction method based on Singular Value Decomposition for radar performance

摘要:

对雷达实施健康管理过程中,预测是重要的功能环节。雷达的性能参数监测序列反映其健康状态,在对其进行建模预测过程中,单一模型难以满足预测准确度要求。为了提高预测准确度,需选用与雷达失效机理相适应的模型。在自回归模型、径向基函数神经网络和奇异值滤波算法的基础上,提出了一种联合两类模型的最优化组合预测方法,将奇异值分解滤波恰当地应用于辨识雷达性能的非同源影响因素并对雷达性能监测序列进行最优拆分。仿真结果表明,该方法相较于单一模型预测和传统的组合预测算法,预测准确度指标提升至少一个数量级。

Abstract:

Abstract: When implementing health management for radar, it is of great significance to introduce the function of fault prediction. When modeling to carry out fault prediction by means of monitoring parameter time series which reflect health of radar system, prediction accuracy of a single model is limited. In order to improve prediction accuracy, models adapted to the failure mechanisms of radar should be considered. On the basis of the introduction of autoregressive model and radial basis function neural network model, an optimized combined prediction algorithm was promoted. Singular value decomposition filtering algorithm was employed to identify factors from different sources which influence radar performance and divide the observation sequence optimally. The simulation results show that compared with algorithm employing a single model and traditional combined prediction algorithm, the prediction accuracy index of the promoted algorithm is improved by at least an order of magnitude.