SAR equipment task failure cause reasoning method based on double-layer nested structure

Abstract

The invention discloses an SAR equipment task failure cause inference method based on a double-layer nested structure, and relates to the technical field of SAR equipment guarantee, wherein the methodcomprises the steps of collecting SAR image data of a known abnormality type; reconstructing a sample category, and extracting a sample set; calculating image quality evaluation characteristics; image feature transformation, normalization processing and class mark combination are carried out; integrating to obtain a training data set and a training double-layer model; processing the unknown abnormal SAR image into a to-be-tested data set; and SAR equipment task failure cause reasoning. A double-layer random forest model is adopted; merging the failure reasons which are easy to mistakenly divide. Therefore, the total category number is reduced; on the basis of the result of the first-layer classifier, image local features are used to carry out second classification on samples which are easy to wrongly classify. Therefore, the abnormal image classification precision of the random forest model is enhanced; the problem that the effect of training the model by using SAR image data of different terrains is poor is solved; and the accuracy of SAR equipment task failure cause reasoning is effectively improved.

Description

Technical field [0001] The invention relates to the technical field of SAR equipment support, in particular to a method for reasoning of SAR equipment task failure based on a double-layer nested structure. Background technique [0002] At present, our army is in a period of transformation from a mechanized army to an informationized army, and the information collection, transmission, and processing capabilities are increasing day by day. As a new type of efficient information acquisition weapon, SAR has become a new way of military observation and reconnaissance. SAR imaging is susceptible to many factors. When the SAR radar returns from a mission and cannot get a high-interpretation, better-effect, and sufficiently clear image, that is, when a mission failure occurs, the radar needs to be detected and repaired. . [0003] Traditional fault diagnosis and troubleshooting techniques mainly rely on expert experience, and have problems of poor reliability, low accuracy, and high fals...

AI Technical Summary

Problems solved by technology

[0003] Traditional fault diagnosis and troubleshooting technologies mainly rely on expert experience, and have problems of poor reliability, low accuracy, and high false alarm rate, which affect the completion of daily drill tasks and even cause irreparable losses to actual military operations. These problems are serious Affects the logistic capability and combat performance of radar equipment, so there is an urgent need for a failure cause reasoning method oriented to mission completion

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