Abstract
This paper focuses on the automatic target recognition (ATR) method based on ship-radiated noise and proposes an underwater acoustic target recognition (UATR) method based on ResNet. In the proposed method, a multi-window spectral analysis (MWSA) method is used to solve the difficulty that the traditional time–frequency (T–F) analysis method has in extracting multiple signal characteristics simultaneously. MWSA generates spectrograms with different T–F resolutions through multiple window processing to provide input for the classifier. Because of the insufficient number of ship-radiated noise samples, a conditional deep convolutional generative adversarial network (cDCGAN) model was designed for high-quality data augmentation. Experimental results on real ship-radiated noise show that the proposed UATR method has good classification performance.
Highlights
Underwater acoustic target recognition (UATR) is a kind of information processing technology that recognizes categories of targets through ship-radiated noise and the underwater acoustic echo received by SONAR
The power spectrum is a stable expression of the ship-radiated noise signal, which can be used as a good classification feature
Mel-frequency cepstrum coefficient (MFCC) is a spectrum feature designed based on human auditory characteristics that has been widely used in feature extraction from audio data, but has been used in feature extraction from ship-radiated noise signals
Summary
Underwater acoustic target recognition (UATR) is a kind of information processing technology that recognizes categories of targets through ship-radiated noise and the underwater acoustic echo received by SONAR. To obtain the time-varying characteristics of the signal, time–frequency analysis is a common feature extraction method for ship-radiated noise. In practice, it is often impossible to obtain a large number of different categories of shipradiated noise signals, and some data augmentation methods are usually needed to expand the sample set. (1) In UATR, a multi-window spectral analysis method is proposed, which c simultaneously extract spectrograms of different resolutions as classification samples. For uniformity of sample set data, the ship-radiated noise signals should be divided into several frames of fixed length through a window of suitable width. According to the MWSA method, three window functions are set to process the sample data, and the three obtained spectrograms are stored in three channels of a color image to form the final sample. The experiment results show that the proposed UATR has good recognition ability for five categories of signals
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