Signal-to-noise ratio evaluation method and device based on unmanned aerial vehicle remote control signal

A technology of remote control signals and drones, applied in the field of deep learning, can solve problems such as large evaluation errors, achieve the effect of improving accuracy, improving accuracy, and reducing evaluation errors

Inactive Publication Date: 2021-02-05
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Experimental data shows that the above two SNR evaluation algorithms have relatively large evaluation errors

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  • Signal-to-noise ratio evaluation method and device based on unmanned aerial vehicle remote control signal
  • Signal-to-noise ratio evaluation method and device based on unmanned aerial vehicle remote control signal
  • Signal-to-noise ratio evaluation method and device based on unmanned aerial vehicle remote control signal

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Embodiment Construction

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0056] In order to achieve the above purpose, an embodiment of the present invention provides a signal-to-noise ratio evaluation method and device based on the remote control signal of a drone. The method and device can be applied to various electronic devices, and the details are not limited. The following first introduces the SNR evaluation method based on the UAV remote control signal in detail.

[0057] figure 1 The first schematic flow chart of the signal-...

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Abstract

The embodiment of the invention provides a signal-to-noise ratio evaluation method and device based on an unmanned aerial vehicle remote control signal. The method comprises the steps: obtaining a to-be-evaluated unmanned aerial vehicle remote control signal, and taking the to-be-evaluated unmanned aerial vehicle remote control signal as a to-be-evaluated signal; inputting the to-be-evaluated signal into a pre-established neural network model to obtain a predicted signal-to-noise ratio of the to-be-evaluated signal; and evaluating the signal-to-noise ratio by utilizing double convolutional neural networks and a double-layer long-short-term memory network which are connected in sequence. The accuracy of predicting the signal-to-noise ratio is improved by continuously adjusting the parameters of the neural network model, so that the accuracy of evaluating the signal-to-noise ratio by the neural network model is improved, and the evaluation error is reduced.

Description

technical field [0001] The present invention relates to the field of deep learning technology, in particular to a signal-to-noise ratio evaluation method and device based on drone remote control signals. Background technique [0002] At present, UAVs are more and more widely used. In some cases, it is necessary to evaluate the signal-to-noise ratio of UAV remote control signals. The evaluation results can help aerospace communication systems detect and identify UAVs to reduce the Interference of cooperative drones on aerial chart-space communications. [0003] In addition, accurate and efficient signal-to-noise ratio estimation can provide information needed for channel allocation for monitoring UAV data links, and can also detect UAV remote control signals. [0004] At present, the main SNR evaluation algorithms include Maximum Likelihood (ML) and Spectrum Analysis (SA). Experimental data show that the evaluation errors of the above two SNR evaluation algorithms are large...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06F30/20G06N3/04
CPCG06F30/20G06N3/044G06N3/045G06F2218/00G06F2218/08
Inventor 景晓军李凌霄穆俊生张荣辉
Owner BEIJING UNIV OF POSTS & TELECOMM
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