In order to solve the problem of active sonar pulse interference existing in ship radiated noise signal, this paper uses Variational Mode Decomposition to decompose the ship radiated noise signal containing pulse interference into multiple Intrinsic Mode Functions. The interference modes are screened out by the relative maximum method, and the effect of the algorithm is evaluated by mean square error and correlation coefficient. The simulation results show that the proposed method can effectively suppress the active sonar pulse interference in ship radiated noise and has a certain robustness. The results of this paper can be used for data preprocessing before underwater acoustic target recognition, and have a certain role in improving the effectiveness of underwater acoustic target recognition.
Sonar pulse detection and recognition is an important research direction of national marine construction. Traditional target detection and recognition methods have insufficient feature extraction capabilities and high time complexity. To solve this problem, this paper makes full use of the strong feature expression capabilities of deep neural networks. Based on the mainstream target detection networks including Faster RCNN, SSD, and YOLOv3, the sonar pulse detection and recognition method based on deep learning are deeply studied and verified on the pulse signal generated by simulation. The experimental results and analysis show that the average detection accuracy of the YOLOv3 network for sonar pulse signals can reach 92.35%, and the detection time of a single pulse signal power spectrum is only 0.018 seconds. Compared with Faster RCNN and SSD, YOLOv3 has better practicability and robustness in the field of sonar pulse detection and recognition.
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