A novel adaptive direct signal interference cancellation method for underwater active electromagnetic detection systems using an error signal power-based variable step-size Fast Block Least Mean Square algorithm

Abstract

As a promising candidate to detect the underwater targets, the underwater active electromagnetic detection system has gained more attention in the field of marine exploration. Because of the separation of the transmitting and receiving antennas, the direct signal interference in the receiving end would be a great challenge during the operation, which severely deteriorates the system’s capability of target identification and localization. This paper proposes a novel direct signal interference cancellation method based on an error signal power-driven variable step-size fast block Least Mean Square (PVSS-FBLMS) algorithm to address the direct signal interference cancellation issue in the underwater active EM detection system. The proposed algorithm aligns well with the system’s characteristic of block-wise data transmission and demonstrates lower complexity, especially for higher FIR filter orders, compared to the Least Mean Square and Normalized Least Mean Square algorithms. Considering the limited time available for direct signal interference cancellation, the proposed algorithm introduces a variable step-size adjustment criterion based on the error signal power to accelerate convergence, while ensuring the robustness of the algorithm. The simulation has been conducted, which demonstrates that the proposed algorithm converges approximate 46.8% faster than that of the improved variable step-size least mean square (IVSS-LMS) and variable step-size least mean square (VSS-LMS) algorithms while its computational complexity is only about 40% that of the two algorithms when the FIR filter order is 128. The direct signal interference cancellation performance of the proposed algorithm is significantly better than that of the IVSS-LMS and VSS-LMS algorithms at low DSI-to-noise ratios under the simulation condition. Additionally, the simulation results show that the proposed algorithm performs steadily in a highly abrupt change of the noise and the DSI’s amplitude. Besides, the in-field experiments are conducted to validate the effectiveness of the proposed algorithm. The experimental results show that the convergence rate of the proposed algorithm is notably faster than that of the IVSS-LMS and VSS-LMS algorithms, with a steady-state output variance on the order of $10^{-6}{V}^2$.