Self-Interference Suppression of Unmanned Underwater Vehicle with Vector Hydrophone Array Based on an Improved Autoencoder

The self-interference of an unmanned underwater vehicle (UUV) weakens its ability to detect targets of interest. Due to limitations in the size of the sonar array and the complexity of the interference, the performance of existing self-interference suppression methods in practical applications is unsatisfactory. Our research focuses on analyzing the influence of near-field interferences on the sample covariance matrix (SCM) and proposes an interference suppression algorithm based on an improved autoencoder. The proposed algorithm effectively learns the feature distribution of near-field interferences within the covariance domain and reconstructs the pure signal covariance matrix through the cancellation of the near-field interference features. Moreover, the proposed algorithm can meet the requirements of real-time processing and does not require prior knowledge about the positions or propagation of interference. Simulations demonstrate that the proposed algorithm outperforms comparison methods, particularly in scenarios with low signal-to-interference ratios and a limited number of sensors. Furthermore, lake experiments provide additional evidence of the proposed algorithm's good performance in practical applications.