An Underwater Acoustic Target Recognition Method Based on Restricted Boltzmann Machine

Abstract

This article focuses on an underwater acoustic target recognition method based on target radiated noise. The difficulty of underwater acoustic target recognition is mainly the extraction of effective classification features and pattern classification. Traditional feature extraction methods based on Low Frequency Analysis Recording (LOFAR), Mel-Frequency Cepstral Coefficients (MFCC), Gammatone-Frequency Cepstral Coefficients (GFCC), etc. essentially compress data according to a certain pre-set model, artificially discarding part of the information in the data, and often losing information helpful for classification. This paper presents a target recognition method based on feature auto-encoding. This method takes the normalized frequency spectrum of the signal as input, uses a restricted Boltzmann machine to perform unsupervised automatic encoding of the data, extracts the deep data structure layer by layer, and classifies the acquired features through the BP neural network. This method was tested using actual ship radiated noise database, and the results show that proposed classification system has better recognition accuracy and adaptability than the hand-crafted feature extraction based method.

Underwater Acoustic Target Recognition with a Residual Network and the Optimized Feature Extraction Method

“…The baseline design is based on the study [16], which shows that using the basic machine learning method, the accuracy is 0.754. Besides, the accuracy achieved by the ResNet18 model as well as that achieved by other state-of-the-art approaches of RBM + BP [18] and RSSD [4] described in the literature are presented in Table 5. Our method achieves an accuracy of 0.943.…”

“…Yang et al [17] propose a so-called competitive Deep Belief Nets (cDBN) for UATR. Luo et al [18] present a UATR method based on Restricted Boltzmann Machine (RBM), which achieves the accuracy of 93.17% on the dataset of ShipsEar. Ke et al [4] propose a novel recognition method of four steps including preprocessing, pretraining, finetuning, and recognition, which achieves the recognition accuracy of 93.28%.…”

An Underwater Acoustic Target Recognition Method Based on Combined Feature With Automatic Coding and Reconstruction

“…DNN has more adjustable parameters, which can effectively use large-scale samples to improve the generalization ability and recognition accuracy of the recognition system. Referring to the network structure of DNN, Reference [20] extracts the features of underwater acoustic signals based on RBM auto-encoder and uses BP classifier to obtain better recognition results than traditional recognition methods. However, the method in [20] only takes a short-time power spectrum as the feature input and ignores the unique rhythmical characteristics of ship radiated noise.…”

“…Referring to the network structure of DNN, Reference [20] extracts the features of underwater acoustic signals based on RBM auto-encoder and uses BP classifier to obtain better recognition results than traditional recognition methods. However, the method in [20] only takes a short-time power spectrum as the feature input and ignores the unique rhythmical characteristics of ship radiated noise. Besides, when the number of samples is small, the recognition effect of this method will decrease obviously.…”

An Underwater Acoustic Target Recognition Method Based on Spectrograms with Different Resolutions

“…In Refs. [4,5], the power spectrum is used as the input of the classifier to achieve a good classification of ship targets. Auditory-based models are also used for feature extraction in underwater acoustic signals.…”