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Method for recognizing underwater acoustic communication modulation mode based on deep learning technique

A deep learning and underwater acoustic communication technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve problems such as instability and randomness, and achieve robust and robust pattern recognition results. The effect of sex and manpower saving

Inactive Publication Date: 2018-05-15
HARBIN ENG UNIV
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Problems solved by technology

[0003] Fan Haibo (Fan Haibo, Yang Zhijun, Cao Zhigang "Automatic Identification of Modulation Modes Commonly Used in Satellite Communications", Journal of Communications, 2004, 25(1): 140-149) proposed a method for automatic identification of communication signal modulation modes based on spectral features. The characteristic parameters that do not require modulation parameters are extracted from the signal power spectrum and square spectrum as feature vectors, which still have a good recognition accuracy at a low SNR. However, the underwater acoustic channel has complex characteristics such as time-varying and space-varying The characteristic parameters of prior knowledge often show serious instability and randomness

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  • Method for recognizing underwater acoustic communication modulation mode based on deep learning technique
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  • Method for recognizing underwater acoustic communication modulation mode based on deep learning technique

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[0044] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0045] The inventive method realization mode is as follows:

[0046] (1) Construct a deep learning convolutional neural network model whose output is the judgment of the sample modulation mode, such as Figure 9 and Figure 10 As shown, a pooling layer is added after each convolutional layer, and the pooling layer is a maximum pooling layer, and the output result of each layer is first normalized and then output;

[0047] (2) Obtain the experimental data or simulation data of underwater acoustic communication signals with different modulation methods, and then use every N sampling points of the data of the same signal as an original data sample;

[0048] (3) Convert all original data samples into the input form of deep learning convolutional neural network, that is, band-pass filter the original data samples first, obtain the filtered result as an N-dimensional ...

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Abstract

The invention relates to a method for recognizing an underwater acoustic communication modulation mode based on a deep learning technique. The method comprises the following steps: constructing a deeplearning convolutional neural network model; presetting a training set sample recognition accuracy rate T and a testing set sample recognition accuracy rate P; acquiring experimental data or simulation data in different modulation modes; carrying out pretreatment by taking N sampling point data as an original data sample; randomly dividing pretreated data samples into a training set and a testingset; training the data by virtue of a training sample set; judging whether the training set sample recognition accuracy rate reaches a preset value, if yes, switching input into a data sample testingset, and testing by virtue of the data sample testing set; otherwise, continuing to train; judging whether the testing set sample recognition accuracy rate reaches a preset value, and if yes, finishing the model; and other wise, acquiring extra data, mixing with the original data, and repeatedly carrying out the method. According to the method, the difficult extraction of signal features caused due to time variation and space variation of an ocean channel is solved.

Description

technical field [0001] The invention relates to an underwater acoustic communication modulation pattern recognition method, in particular to an underwater acoustic communication modulation pattern recognition method based on deep learning technology, which belongs to the field of underwater acoustic communication and pattern recognition. Background technique [0002] As an important part of the field of underwater acoustic countermeasures, research on modulation pattern recognition of non-cooperative underwater acoustic communication signals has increasingly become an important research topic. In order to effectively realize classification recognition, it is necessary to obtain the features that can best reflect the difference of signal classification. [0003] Fan Haibo (Fan Haibo, Yang Zhijun, Cao Zhigang "Automatic Identification of Modulation Modes Commonly Used in Satellite Communications", Journal of Communications, 2004, 25(1): 140-149) proposed a method for automatic...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F2218/12G06F18/24133
Inventor 殷敬伟邵梦琦韩笑周启明李成沈益冉李理
Owner HARBIN ENG UNIV
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