Abstract
Underwater acoustic target recognition is one of the main functions of the SONAR systems. In this paper, a target recognition method based on combined features with automatic coding and reconstruction is proposed to classify ship radiated noise signals. In the existing underwater acoustic target recognition systems, the target category features are mostly constructed based on the power spectrum according to a certain presupposed model, and some useful information in the data is discarded artificially. In the proposed recognition method, a feature extractor based on auto-encoding is designed. The feature extractor uses the restricted Boltzmann machine (RBM) to automatically encode the combined data of the power spectrum and demodulation spectrum of ship radiated noise without supervision and extracts the deep data structure layer by layer to obtain the signal feature vector. The extracted feature vector is sent to a Back Propagation (BP) neural network to realize target recognition. Due to the high cost of ship radiated noise acquisition, the sample size of ship radiated noise signals is often hard to meet the needs of neural network training. A method of data augmentation is designed by RBM auto-encoder to construct the expanded sample set, which improves the performance of the recognition system. The experimental results based on the actual ship’s radiated noise show that the proposed method has better performance than the traditional methods.
Highlights
Sound is the only known form of energy that can travel long distance underwater
Using the underwater acoustic signals received by hydrophones, the underwater acoustic target recognition system analyzes the characteristics of underwater targets and distinguishes the types of targets by signal processing methods [1]–[3]
The input of the model is the spectrum of the underwater acoustic signal, and the high-level features of data are extracted by layer by layer auto-coding of stacked restricted Boltzmann machine (RBM), and the target recognition is based on these features by Back Propagation (BP) neural network
Summary
Sound is the only known form of energy that can travel long distance underwater. The classification and recognition of underwater acoustic targets are of great significance in the monitoring of ships at sea, the search for underwater targets, and maritime law enforcement, etc. In underwater acoustic target recognition, RBM auto-encoder can effectively reduce the dimension of the original spectrum data and extract the high-level data distribution characteristics of the original data [16], which is helpful for subsequent pattern recognition. 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. An auto-encoder based on the Boltzmann machine is constructed to extract the adaptive features of the power spectrum and demodulation spectrum data of underwater acoustic signals.
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