Diagnosis Method of Radar Abnormal State Based on Deep Learning

Abstract

The invention discloses a radar abnormal state diagnosis method based on deep learning. The feature parameters related to each type of fault are extracted by using the method; for each type of fault, the feature parameter with the largest correlation coefficient among the feature parameters is taken as the reconstruction parameter target of the reconstruction model, and the reconstruction model is built using the long short-term memory network LSTM model. Use the characteristic parameters other than the characteristic parameter with the largest correlation coefficient to fit and reconstruct the characteristic parameter with the largest correlation coefficient to obtain the reconstruction value; do probability-based quantification for the difference between the reconstruction value and the measured value of each type of fault Standard; make time interval statistics on the quantitative results of each type of fault, integrate the diagnosis results of different models, obtain real-time diagnosis results of multiple faults and give early warning, filter out false alarms, and obtain the final diagnosis result.

Description

technical field [0001] The invention belongs to the technical field of radar systems, and in particular relates to a method for diagnosing abnormal states of radars based on deep learning. Background technique [0002] The natural weather radar system currently in use is a relatively complex electronic system, including a transmitter subsystem, a receiver subsystem, and a servo subsystem. There is no physical connection between the electronic parameters of each subsystem, so the electronic There is no physical connection between parameters, which makes it impossible to use traditional expert experience to diagnose and predict the faults of natural weather radar systems. Contents of the invention [0003] In view of the above existing technical problems, the present invention provides a method for diagnosing abnormal states of radar based on deep learning. [0004] In order to solve the problems of the technologies described above, the present invention adopts the followin...

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Problems solved by technology

[0002] The natural weather radar system currently in use is a relatively complex electronic system, including a transmitter subsystem, a receiver subsystem, and a servo subsystem. There is no physical connection between the electronic parameters of each subsystem, so the electronic There is no physical connection between parameters, making it impossible to use traditional expert experience for fault diagnosis and prediction of natural weather radar systems

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