【摘要】 针对水下航行器等平台存在宽带背景噪声干扰导致目标检测能力下降问题,提出一种适用于多通道水听器阵列的深度学习宽带背景噪声干扰抑制方法。该方法通过含干扰信号的多通道频域特征级联保留了相位信息,使用深度复数神经网络建立了一个估计纯净目标信号频谱特征的学习模型实现干扰抑制,再利用常规波束形成方法实现目标检测与跟踪。仿真结果表明,在信干比为-15 dB,-20 dB,-25 dB的双目标信号条件下,所提干扰抑制方法可以有效减少近场干扰影响,提高了常规波束形成的检测能力。湖试结果表明,该方法能够自适应地实现平台宽带背景噪声干扰抑制,有效提升目标检测和跟踪性能。更多还原

【Abstract】 Aiming at the problem of the decline of target detection capability caused by broadband background noise interference in underwater vehicles and other platforms, a deep learning broadband background noise suppression method suitable for multi-channel hydrophone arrays is proposed. This method preserves the phase information by cascading the multi-channel frequency domain characteristics of the interference signal, and a learning model is established to estimate the spectral characteristics of pure target signals by using a deep complex neural network to achieve interference suppression, and then the conventional beamforming method is used to achieve object detection and tracking. The simulation results show that under the dual target signal conditions of-15 dB,-20 dB and-25 dB signal-to-interference ratio, the proposed method can effectively reduce the influence of near-field interference and improve the detection ability of conventional beamforming. The lake test results show that the proposed method can adaptively realize the suppression of platform broadband background noise interference, and effectively improve the target detection and tracking performance.更多还原