Fast and accurate identification method of underwater acoustic signal modulation mode based on deep hybrid neural network

A hybrid neural network and modulation method recognition technology, which is applied in modulation type recognition, neural learning methods, biological neural network models, etc., can solve the problems of low recognition accuracy, high computing cost, and poor generalization performance, and achieve high recognition Accuracy, network accuracy improvement, and the effect of high accuracy

Active Publication Date: 2022-04-26
QINGDAO UNIV OF SCI & TECH
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Problems solved by technology

[0004] Aiming at technical problems such as poor generalization performance, high calculation cost, and low recognition accuracy of existing underwater acoustic signal modulation method recognition methods, the present invention proposes a fast and accurate underwater acoustic signal modulation method recognition method based on a deep hybrid neural network, which can solve the above problems

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  • Fast and accurate identification method of underwater acoustic signal modulation mode based on deep hybrid neural network
  • Fast and accurate identification method of underwater acoustic signal modulation mode based on deep hybrid neural network
  • Fast and accurate identification method of underwater acoustic signal modulation mode based on deep hybrid neural network

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Embodiment 1

[0074] In an underwater acoustic communication system, the transmitting and receiving ends usually agree on the modulation mode through the handshake signal. However, the underwater environment is complex and changeable, which seriously interferes with the handshake signal and causes errors. Therefore, the receiving end can automatically identify the modulation mode of the received signal through the intelligent identification method of the modulation mode, so as to ensure the accuracy of the demodulation of the underwater acoustic signal.

[0075] A fast and accurate underwater acoustic signal modulation method recognition method based on deep hybrid neural network, comprising the following steps:

[0076] Underwater acoustic signal preprocessing steps, such as figure 1 shown, including:

[0077] S1. Perform normalization operation and variable dimension processing on the signal;

[0078] The normalization operation formula is:

[0079]

[0080] Among them, S is the ori...

Embodiment 2

[0143] In order to specifically verify the modulation mode recognition effect of the present invention, this embodiment conducts specific experiments based on actual South China Sea sea test data. The specific implementation method of this embodiment is the same as that of embodiment 1. When communicating under the sea, the sending end sends a modulated underwater acoustic signal, and the receiving end automatically recognizes the modulation mode of the underwater acoustic signal and demodulates the signal correctly.

[0144] Based on the actual sea trial data in the South China Sea (including 8 modulation signals of BFSK, QFSK, BPSK, QPSK, 16QAM, 64QAM, OFDM and DSSS, each type of modulation signal has 200), the identification results of this embodiment are shown in Table 3, Figure 4 Shown:

[0145] Table 3 is based on the performance of the neural network of the present invention based on the South China Sea sea test data

[0146]

[0147] Depend on Figure 4 , Table ...

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Abstract

The invention discloses a fast and accurate identification method of underwater acoustic signal modulation mode based on a deep hybrid neural network. The method includes: preprocessing the received underwater acoustic signal; building a deep hybrid neural network; training the hybrid neural network; The preprocessed underwater acoustic signal is input to the trained neural network to identify the modulation mode of the underwater acoustic signal. The present invention does not rely on traditional methods to select and extract features, and adopts deep learning methods to automatically extract features related to modulation modes to ensure the effectiveness of feature extraction; a deep hybrid neural network model is designed according to the timing characteristics of underwater acoustic signals, and tested on sea test data The set has a high recognition accuracy rate; the convolutional layer of the deep hybrid neural network is improved by de-pooling and one-dimensional convolution, and the network accuracy is improved by increasing the network width, and the recognition speed is taken into account while ensuring the recognition accuracy. The invention finally realizes a low-delay, high-accuracy underwater acoustic signal modulation method recognition method.

Description

technical field [0001] The invention belongs to the technical field of deep learning and communication, and in particular relates to a fast and accurate underwater acoustic signal modulation method recognition method based on a deep hybrid neural network. Background technique [0002] Underwater wireless data transmission technology plays a vital role in both civilian and military fields. Underwater acoustic communication has become the most widely used underwater communication method due to its advantages of low propagation loss and long transmission distance. At present, Adaptive modulation coding (AMC) technology, which can select the modulation mode according to the channel conditions, has been widely used in underwater acoustic communication systems. This technology requires the receiving end and the sending end to confirm the modulation of the communication signal through multiple handshake signals. However, the underwater environment is complex and changeable, result...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L27/00H04B11/00H04B13/02G06N3/04G06N3/08
CPCH04L27/0012H04B11/00H04B13/02G06N3/04G06N3/08
Inventor 王景景张威龙董新利
Owner QINGDAO UNIV OF SCI & TECH
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