Underwater Acoustic Signal Prediction Based on Correlation Variational Mode Decomposition and Error Compensation

Abstract

Underwater acoustic signal is highly complex and difficult to predict. To improve the prediction accuracy of underwater acoustic signal, a complex underwater acoustic signal prediction method combining correlation variational mode decomposition (CVMD), least squares support vector machine (LSSVM) and Gaussian process regression (GPR) is proposed. Aiming at the problem of sample partitioning, this paper proposes a method of obtaining the embedding dimension and time delay based on the extreme learning machine prediction model. By selecting the appropriate time delay and embedding dimension, the prediction accuracy has improved. Aiming at the K-value selection of variational mode decomposition (VMD), this paper proposes a CVMD decomposition method, which improves the adaptability of VMD algorithm by selecting K-value through the correlation coefficient. Firstly, CVMD is used to decompose the underwater acoustic time series into several different components. Then, LSSVM prediction models are established for each component. Finally, to further improve the prediction accuracy of the model, Gaussian process regression (GPR) is used to correct the prediction result. One-step and multi-step prediction of underwater acoustic time series is carried out in this paper. Simulation results show that the model proposed in this paper has high prediction accuracy and can be effectively used in underwater acoustic signal prediction.

Keywords

• Underwater acoustic signal
• variational mode decomposition
• least squares support vector machine
• Gaussian process regression
• prediction