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.

Based on the current mainstream Extract Transform Load (ETL) tools for data and the disadvantages of some applications in restricted environments, a Regular Expression Mass-data Cleaning System (REMCS) was proposed for the specific requirements in the restricted application scenarios. Firstly, the data features of six typical problems including ultra-long error data, batch fusion of data source files, automatic sorting of data source files, were discovered. And the appropriate regular expressions and pre-processing algorithms were put forward according to the data features. Then, data pre-processing was completed by using the algorithm model to remove the errors in data. At the same time, the system logical structure, common problems, and corresponding solutions, and code implementation scheme of REMCS were described in detail. Finally, the comparison experiments of several common data processing problems were carried out with the following four aspects:the compatible data source file formats, the soveble problem types, the problem processing time and the data processing limit value. Compared with the traditional ETL tools, REMCS can support nine typical file formats such as csv format, json format, dump format, and can address all six common problems with shorter processing time and larger supportable data limit value. Experimental results show that REMCS has better applicability and high accuracy for common data processing problems in restricted application scenarios.