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

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...

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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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