Method for automatically identifying and learning abnormal radio signal type

Abstract

The invention discloses a method for automatically identifying and learning abnormal radio signal types. The method is characterized by completing feature extraction of a radio signal in virtue of analysis and actual measurement of the frequency spectrum data of the radio data in combination with experiential knowledge of radio monitoring experts and a feature extraction method. In the characteristic space of the radio signal, clustering analysis is executed on actually-measured sweep-frequency interference, broadband interference, narrowband interference, and invalid intervention signals by using a clustering analysis method. By means of a result of the clustering analysis, the method may provide a radio monitoring device with a capability of automatically identifying abnormal radio signal types. With accumulation of the frequency spectrum data of the actually-measured radio data and increase in identification errors, the method provides a capability of automatically learning the results of the clustering analysis regularly. According to an identification result in combination with a communication device, the method provides an automatic alarming capability for a monitoring device. The method, used in a radio monitoring device, improves an information processing capability of the radio monitoring device, may achieve unmanned operation of the radio monitoring device, and decreases the workload of monitoring technicians.

Description

Technical field [0001] The invention relates to the field of radio monitoring, and more specifically to the detection of radio signals and automatic identification of abnormal radio signals. Background technique [0002] Radio monitoring mainly focuses on radio signals in radio management areas, and analyzes, identifies, monitors and obtains technical information such as technical parameters, working characteristics, and radiation positions of the radio signals. The discovery and identification of abnormal radio signals occupies an important position in radio monitoring, especially the monitoring of abnormal radio signals during major events. [0003] Traditional radio monitoring work is done manually by radio monitoring personnel through monitoring equipment, with professional knowledge and actual experience of the monitoring personnel. And it is mainly judged by the spectrogram and duration of the signal. This identification method mainly has the following shortcomings: On the ...

AI Technical Summary

Problems solved by technology

This identification method mainly has the following shortcomings: on the one hand, different staff have different experience and knowledge, and there is a certain degree of subjectivity in making decisions, which directly affects the correct identification of abnormal radio signals; The discovery and identification of abnormal signals depends on technicians, and most of the work is repeated, which is a waste of human resources and greatly increases the difficulty and workload of their work in emergency situations; on the other hand, abnormal radio The identification of signals follows a set of standardized monitoring procedures. Due to the insufficient information processing capability of existing radio monitoring equipment, the identification of abnormal radio signals is heavily dependent on technicians, and it is impossible to realize the unmanned operation of radio monitoring equipment.

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