Short-wave signal detection and identification method based on computer vision

Abstract

The invention discloses a short-wave signal detection and identification method based on computer vision. The invention provides the short-wave signal detection and identification method based on computer vision according to a time frequency matrix characteristic of a short wave signal and a classifier construction process of machine vision. Compared with a traditional signal detection determining manner based on an artificial manner and other existing signal detection determining manners, the method of the invention has an advantage of realizing automatic detection and identification for short-wave signals. Furthermore compared with other signal detection and identification methods, the short-wave signal detection and identification method has advantages of lower requirement for a noise environment in which the signal exists, and high suitability for a channel environment with noise which reaches 0dB. After a large number of actual data tests in different channel conditions, detection and identification correct rate of the short-wave signals are above 90%.

Description

Technical field [0001] The present invention relates to the field of computer vision technology and machine learning, and in particular to a detection method for realizing shortwave signal positioning and common type recognition by classifying shortwave signal features extracted based on computer vision through SVM (Support Vector Machine). Background technique [0002] Shortwave signals are one of the most common signal collections in radio communication. According to the division of the International Radio Advisory Committee (CCIR), shortwave is defined as electromagnetic waves with a wavelength of 100m to 10m and a frequency of 3MHz to 30MHz. Radio communications using shortwaves become shortwave communications, also known as high frequency (HF) communications. In normal use, in order to make full use of the advantages of short-wave short-range communication, the actual frequency range of short-wave communication is 1.5MHz ~ 30MHz. [0003] The technology involved in the presen...

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Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a short-wave signal modeled by the computer vision feature of the signal and judged by the SVM classifier for the problem of high time-consuming and high complexity in manual detection and identification of short-wave signals in noisy communication channels. The method of automatic signal detection and recognition can effectively improve the timeliness of the process while ensuring the accuracy of shortwave signal detection and recognition

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