Wireless signal area intensity detection method based on deep learning

Abstract

The invention discloses a wireless signal area intensity detection method based on deep learning. The method comprises the steps of carrying out short-time Fourier transform to obtain a two-dimensional signal speech graph; cutting, filling and deforming to obtain a speech graph sample; making a data set label; training the customized data set by using a deep learning one-dimensional compression model to obtain a model weight file; performing pretreatment; predicting the preprocessed picture to obtain regional signal intensity probability output; and obtaining position information of the signal and an image quality prediction value of the signal, and obtaining an optimal region. According to the invention, signal detection under complex conditions can be processed, and the robustness is very high; intensity probability output is carried out on each region of the signal, signal detection is realized, the pertinence is strong, and the false detection rate is low; and the position information of the signal can be obtained, and the quality evaluation of each region of the signal can also be obtained.

Description

technical field [0001] The invention belongs to the technical field of wireless signal processing, and in particular relates to a method for detecting the strength of a wireless signal area based on deep learning. Background technique [0002] Radio communication is widely used in many fields such as diplomacy, meteorology, post and telecommunications, military, transportation, etc., to transmit information such as images, data, language, and text. Due to factors such as path attenuation, atmospheric noise, time delay, ionospheric fading, and multipath effects, shortwave signals will be distorted and weakened during propagation, which will affect the effect of radio communication. At the same time, in the radio communication scenario, it is often necessary for personnel to observe the received spectrum through experience to identify the signal and mark the position. [0003] At this stage, deep learning technology is mature in image recognition. Combining deep learning tech...

AI Technical Summary

Problems solved by technology

Since the radio signal is in the general deep learning detection algorithm, the sample marking method is frame selection, and this marking method will greatly affect the detection effect

[0004] Using the general-purpose deep learning target detection method to detect signal language graphs will inevitably include background noise into the frame when making frame selection marks due to the discontinuity, incompleteness, and low signal-to-noise ratio of the actual signal. This method will lead to a high false detection rate, so general deep learning marking and detection methods are not suitable for signal detection

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