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Unmanned aerial vehicle spectrum detection method based on deep learning

A detection method and deep learning technology, applied in the field of UAV detection, can solve the problems of many false positives, few UAV signal features, and difficulty in extracting UAV signal features, and achieve the effect of simple principle and easy operation

Inactive Publication Date: 2019-04-12
HUNAN NOVASKY ELECTRONICS TECH
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AI Technical Summary

Problems solved by technology

[0014] b) The diversity of UAV signals makes it difficult to extract UAV signal features;
[0015] c) Manual extraction of drone signal features is less, and in complex electromagnetic environments, there are more false positives

Method used

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  • Unmanned aerial vehicle spectrum detection method based on deep learning
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  • Unmanned aerial vehicle spectrum detection method based on deep learning

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Embodiment Construction

[0037] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0038] Such as figure 1 Shown, a kind of UAV frequency spectrum detection method based on deep learning of the present invention, its step comprises:

[0039] Step S1, UAV signal training set collection;

[0040] Step S2, UAV signal preprocessing;

[0041] Step S3, building a multi-layer convolutional neural network model for spectrum sensing, using the convolutional neural network to extract features, and extracting multi-level signal features through multi-level convolutional layers and pooling layers. Among them, the pooling layer appears in pairs after the convolutional layer, the convolutional layer extracts the signal features, and the pooling layer extracts the features of the convolutional output Figure 1 On the one hand, compression is performed to simplify the computational complexity; on the other hand, feature compress...

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Abstract

The invention discloses an unmanned aerial vehicle spectrum detection method based on deep learning. The method comprises the following steps: S1, collecting an unmanned aerial vehicle signal trainingset; s2, unmanned aerial vehicle signal preprocessing; step S3, constructing a multi-layer convolutional neural network model for spectrum sensing, extracting features by using the convolutional neural network, and extracting multi-level signal features through a multi-level convolutional layer and a pooling layer; s4, training the convolutional neural network model constructed in the step S3 byusing the sample in the step S2; s5, an online detection stage; and judging whether the signal is the unmanned aerial vehicle signal or not by using the trained convolutional neural network model to realize the detection of the unmanned aerial vehicle spectrum. The method has the advantages of simple principle, simplicity and convenience in operation, capability of realizing quick and accurate alarm of the unmanned aerial vehicle and the like.

Description

technical field [0001] The present invention mainly relates to the field of unmanned aerial vehicle detection technology, in particular to a deep learning-based unmanned aerial vehicle spectrum detection method. Background technique [0002] At present, drones are used more and more widely, and have become professional equipment and living tools in various industries, which can greatly save working time and improve work efficiency. But in some key areas, such as airports, prisons, government departments, etc., drones have brought security risks to these sensitive areas. Drones can smuggle drugs and weapons, spy on privacy, and affect the take-off and landing of aircraft. Therefore, it is necessary to detect drones in the air and false alarm in advance. [0003] For the detection of drones, due to the poor detection and recognition capabilities of traditional radars and poor environmental adaptability, the current mainstream uses spectrum passive detection technology to det...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04
CPCG06N3/045G06F2218/12
Inventor 王山韩乃军韩明华
Owner HUNAN NOVASKY ELECTRONICS TECH
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