This paper addresses the challenges faced by towed array sonar systems, including near field interference from platform noise and beamforming distortion from array irregularities. A novel approach is proposed, combining spatial matrix filtering and plane wave azimuth estimation technology. By leveraging the characteristics of near field platform noise and far field target signals, an array data reconstruction filter is designed to mitigate interference before target azimuth estimation. This method aims to enhance target detection performance by suppressing platform noise and improving array data reconstruction.
KEYWORDS: Modulation, Feature extraction, Acoustics, Machine learning, Orthogonal frequency division multiplexing, Interference (communication), Education and training, Signal to noise ratio, Image information entropy, Detection and tracking algorithms
Automatic classification and recognition of underwater acoustic communication signal modulation plays a key role in the field of underwater communication and confrontation, and it is a necessary combat capability in modern naval warfare. However, the recognition method based on Gaussian white noise environment established on the basis of traditional theory is still difficult to recognize in the background of underwater impulse noise. Modulation identification faces enormous challenges. In response to this problem, this study proposes an ensemble learning classification algorithm based on the fusion of convolutional neural network feature extraction and artificial feature extraction. Under small sample conditions, the simulation results show that the recognition rate of the ensemble learning method after feature fusion in the mixed Signal samples with multiple signal-to-noise ratios is improved to 99.7%, and it is robust to underwater impulse noise interference.
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