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

Underwater acoustic signal target classification and recognition method based on deep learning

An underwater acoustic signal and target classification technology, applied in character and pattern recognition, instruments, biological neural network models, etc. problem, to achieve the effect of improving the accuracy and improving the robustness

Active Publication Date: 2019-05-24
HARBIN ENG UNIV
View PDF6 Cites 25 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the classification and recognition of underwater targets, classification and recognition methods such as neural network, hidden Markov model and support vector machine (SVM) are usually used. These traditional underwater acoustic signal target classification and recognition methods use shallow structure algorithms to complete the classification and recognition of targets. Lack of the ability to learn the essential characteristics of the data set from a large number of data samples. At present, the types of underwater acoustic signal targets are complex, coupled with the complex environment of the seabed and a lot of noise, which makes the traditional classification and recognition methods unable to accurately complete the underwater acoustic signal. Target Classification and Recognition

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 signal target classification and recognition method based on deep learning
  • Underwater acoustic signal target classification and recognition method based on deep learning
  • Underwater acoustic signal target classification and recognition method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] The present invention is described in more detail below in conjunction with accompanying drawing example:

[0038] combine figure 1 , the concrete steps of the present invention are as follows:

[0039] (1) Use the GFCC algorithm to extract the features of the original underwater acoustic signal

[0040] The Gammatone filter is a causal filter with an infinitely long sequence impulse response. In the filter bank, the time-domain impulse response of each Gammatone filter i can be regarded as the product of the Gamma function and the acoustic signal, defined as:

[0041]

[0042] In the formula: n is the order of the filter, b i Indicates the attenuation factor of the filter, f i is the center frequency expressed in Hz, Represents the phase of the filter, u(t) is a step function, and N represents the total number of filters.

[0043] In the process of underwater acoustic signal feature extraction, in order to simulate the auditory characteristics of the human ear...

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 belongs to the technical field of underwater acoustic signal processing, and particularly relates to an underwater acoustic signal target classification and recognition method based on deep learning. The method comprises the following steps: (1) carrying out feature extraction on an original underwater acoustic signal through a Gammatone filtering cepstrum coefficient (GFCC) algorithm; (2) extracting instantaneous energy and instantaneous frequency by utilizing an improved empirical mode decomposition (MEMD) algorithm, fusing the instantaneous energy and the instantaneous frequency with characteristic values extracted by a GFCC algorithm, and constructing a characteristic matrix; (3) establishing a Gaussian mixture model GMM, and keeping the individual characteristics of theunderwater acoustic signal target; And (4) finishing underwater target classification and recognition by using a deep neural network (DNN). According to the underwater acoustic signal target classification and recognition method, the problems that a traditional underwater acoustic signal target classification and recognition method is single in feature extraction and poor in noise resistance can be solved, the underwater acoustic signal target classification and recognition accuracy can be effectively improved, and certain adaptability is still achieved under the conditions of weak target acoustic signals, long distance and the like.

Description

technical field [0001] The invention belongs to the technical field of underwater acoustic signal processing, and in particular relates to an underwater acoustic signal target classification and recognition method based on deep learning. Background technique [0002] The development and utilization of marine resources is an important way to achieve sustainable development. Underwater target classification and recognition technology can help to better conduct marine biological surveys and marine exploration. The ocean environment is complex and changeable, electromagnetic waves are greatly attenuated in water, and most underwater targets have their own specific acoustic characteristics, so it is more appropriate to choose the signal form of sound waves to study underwater targets. In the military field, underwater acoustic signal target classification and recognition technology can detect underwater targets accurately, timely, and covertly, and provide accurate information fo...

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): G06K9/00G06K9/62G06N3/02
Inventor 王兴梅刘安华孟稼祥
Owner HARBIN ENG 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