Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Underwater acoustic target recognition method based on deep convolutional generative adversarial network

A technology of target recognition and deep convolution, applied in biological neural network models, neural learning methods, character and pattern recognition, etc., can solve problems such as relying on manual experience, cumbersome feature extraction procedures, and difficult to fully and effectively use

Inactive Publication Date: 2019-11-22
NORTHWESTERN POLYTECHNICAL UNIV
View PDF5 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

It can solve the disadvantages of cumbersome feature extraction procedures in existing methods, relying on human experience, and difficulty in making full and effective use of non-category information data, and can improve the accuracy of classification and recognition compared with the baseline model

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Underwater acoustic target recognition method based on deep convolutional generative adversarial network
  • Underwater acoustic target recognition method based on deep convolutional generative adversarial network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] Combine below figure 1 The present invention is further described.

[0025] Using python as the operating platform, the detailed implementation steps and calculation methods of this method are listed.

[0026] Introduction to the database of this example: The lake test data set is divided into 4 categories, which are the radiation noise of 4 different types of ships. The types of ships are iron boat, Shuhang, Cathay and New Century. Array for round-the-clock data collection. In this experiment, the data of one of the channels of one of the linear arrays is intercepted, and the data of each type of target is intercepted for 20 minutes, and the sampling frequency is 48kHz.

[0027] Such as figure 1 Shown:

[0028] Step 1: Normalize the original underwater acoustic signal, divide into frames with a frame length of 0.01s and a frame overlap of 0.005s, and obtain 240,000 samples of each category, a total of 960,000 samples. Among them, 40,000 samples are randomly select...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to an underwater acoustic target recognition method based on a deep convolution generative adversarial network, and belongs to the field of underwater acoustic target recognition. The method comprises the following steps: constructing a generation model and a discrimination model; normalizing the original underwater acoustic signals with the category information and the original underwater acoustic signals without the category information, and framing; setting generation model parameters; setting hyper-parameters; constructing a convolutional neural network in the discrimination model; taking the signal data as the input of a convolutional neural network, and calculating and outputting the signal data through the network to obtain a classification result; and countingclassification errors, returning the errors by using a BP algorithm, and updating the weight parameters of the network in the three steps, including the weight between the convolution kernel and thefull connection layer, until the iteration frequency is reached. The method has the advantages that the extracted features completely depend on the data, parameters related to the data do not need tobe set manually, the extracted features are effective to a certain extent for the data, and the data can be effectively utilized to mine distribution information existing in the data.

Description

[0001] Field [0002] The invention relates to an underwater acoustic target recognition method based on a deep convolution generation confrontation network, belonging to the field of underwater acoustic target recognition. Background technique [0003] Due to the development of technology and the needs of practical engineering applications, underwater acoustic target recognition faces two important problems: 1. Since manual calibration of a large number of acoustic target data is a costly task, generally only a few of the same samples can be Some of them are manually calibrated, so for the acoustic samples that have not been calibrated during the collection process, based on the consideration of making full use of the acoustic signal samples, it is necessary to propose an identification scheme for unknown targets; 2. The underwater acoustic targets are diverse, and for a certain target or It is difficult for the features extracted from the dataset to include all possible obje...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/2411
Inventor 曾向阳汪鑫王海涛陆晨翔周治宇黄擎
Owner NORTHWESTERN POLYTECHNICAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products