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Underwater acoustic target recognition method based on weighted support vector machine

A support vector machine and target recognition technology, applied in the field of underwater acoustic target recognition based on weighted support vector machine, can solve the problems of variable working conditions of underwater acoustic targets, low recognition efficiency, complex sample data of marine environment channels, etc.

Active Publication Date: 2020-09-25
HARBIN ENG UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of poor robustness of the target classifier and low recognition efficiency caused by factors such as variable underwater acoustic target conditions, complex marine environment channels, and difficult acquisition of sample data. Underwater Acoustic Target Recognition Method Based on Vector Machine

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  • Underwater acoustic target recognition method based on weighted support vector machine
  • Underwater acoustic target recognition method based on weighted support vector machine
  • Underwater acoustic target recognition method based on weighted support vector machine

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[0105] Step 1. For a binary underwater acoustic target recognition problem, the existing class A target is the radiated noise data of a cargo ship with a duration of 600s, and the target of type B is the radiated noise data of a motorboat with a duration of 900s. First, the continuous signal is divided into frames. After the frame is divided, the length of each frame is called "frame length", and the number of frame sequences is called "frame number". Set the signal frame length of the frame processing to 0.5s, and each frame sequence is regarded as a sample, thus obtaining 1200 type A sample frame sequences and 1800 type B sample frame sequences, and constructing the underwater acoustic target sample library. Since the method of the present invention belongs to supervised learning, it is necessary to mark the class A samples as "-1" and the class B samples as "+1" to generate a one-to-one corresponding label matrix Y={-1,+1}.

[0106] Step 2. According to the sample frame seq...

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Abstract

The invention provides an underwater acoustic target recognition method based on a weighted support vector machine. The method comprises the steps of performing framing preprocessing, performing feature extraction, building a weighted support vector machine recognition model, searching an optimal kernel function parameter and a penalty factor by utilizing a grid search method, training the weighted support vector machine recognition model, reflecting a recognition result of a classifier on an underwater acoustic target through a confusion matrix, and counting the recognition accuracy of the classifier. According to the method, an appropriate feature extraction method is selected for the characteristics of the underwater acoustic target, the method has the capability of autonomously selecting model parameters, the correct recognition rate of the underwater acoustic target is 80% or above, and the stability of a classifier is higher than that of an existing classification method.

Description

technical field [0001] The invention belongs to the technical field of underwater acoustic target recognition, in particular to an underwater acoustic target recognition method based on a weighted support vector machine. Background technique [0002] With the advancement of sonar technology, signal detection and estimation, computer processing and other technologies, underwater acoustic target recognition technology has developed into an independent discipline. This research explores the classification and recognition of underwater targets from three directions: target characteristic analysis, target feature extraction, and target recognition classifier selection and design. With the development of modern underwater acoustic signal processing technology, various classifiers are continuously applied to the underwater acoustic target recognition system, which makes the underwater target recognition technology develop rapidly towards the trend of intelligence and autonomy. At ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G01S7/539G01H17/00
CPCG01S7/539G01H17/00G06F2218/12G06F2218/08G06F18/2411G06F18/2415G06F18/214
Inventor 齐滨梁国龙付进孙金王燕王逸林张光普王晋晋邹男
Owner HARBIN ENG UNIV
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