Wireless signal area intensity detection method based on deep learning

A technology of deep learning and detection methods, applied in neural learning methods, transmission monitoring, biological neural network models, etc., can solve the problems of high false detection rate, affecting the effect of detection, not suitable for signal detection, etc., to achieve low false detection rate. , good effect, targeted effect

Active Publication Date: 2021-10-26
电信科学技术第五研究所有限公司
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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 incl

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  • Wireless signal area intensity detection method based on deep learning
  • Wireless signal area intensity detection method based on deep learning
  • Wireless signal area intensity detection method based on deep learning

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Embodiment Construction

[0022] The present invention will be further described below in conjunction with accompanying drawing:

[0023] as attached figure 1 As shown, the present invention is a method for detecting the strength of a wireless signal area based on deep learning, comprising steps:

[0024] Step 1: Perform short-time Fourier transform on the signal data to obtain a two-dimensional signal language graph;

[0025] Step 2: Cut, fill and deform the two-dimensional signal language graph to obtain a language graph sample;

[0026] Step 3: Divide each sample into N areas on average, grade the signal area according to the strength of the signal area, mark each area of ​​the training sample according to the level, and make it as a data set label;

[0027] Step 4: Use the deep learning one-dimensional compression model to train the customized data set, stop the training after the loss value converges, and obtain the model weight file;

[0028] Step 5: Preprocessing the predicted signal through ...

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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...

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Application Information

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IPC IPC(8): H04B17/318G06N3/04G06N3/08
CPCH04B17/318G06N3/08G06N3/045
Inventor 王圣川王珂景亮张俊
Owner 电信科学技术第五研究所有限公司
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