Owing to the strong reverberation and various obstacle echoes interference in harbor scenes, the active detection performance of tracking methods for weak targets will be seriously reduced. Moreover, the conventional dereverberation and tracking methods generally cannot effectively separate the weak target under the overlapping echoes of multiple scattering sources. Focusing on solving the above problems, the correlation residual cumulative clustering (CRCC) algorithm is proposed to extract scattering features of weak target motion. There are two innovations in this letter: First, the complex cepstrum filter is improved to suppress strong reverberation. Second, the correlation residual accumulation of adjacent frames is extracted to detect the reduction matrix containing the target trajectory. Finally, the fuzzy C-means (FCM) objective function is optimized via spatial–temporal constraint, thus the weak target can be effectively separated from the overlapping giant interference. The experimental results indicate the strong antijamming, low detection loss, and short-term accumulation of our proposed model, which are remarkably superior to the traditional posterior probability models.

中文翻译：

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多源干扰下港口场景小体积弱目标回波水下检测


由于港口场景中存在强烈的混响和各种障碍物回波干扰，弱目标跟踪方法的主动检测性能会严重下降。而且，传统的去混响和跟踪方法通常无法在多个散射源的重叠回波下有效分离弱目标。针对上述问题，提出了相关残差累积聚类（CRCC）算法来提取弱目标运动的散射特征。这封信有两个创新点：一是改进了复数倒谱滤波器，抑制强混响。其次，提取相邻帧的相关残差累加来检测包含目标轨迹的约简矩阵。最后，通过时空约束对模糊C均值（FCM）目标函数进行优化，从而可以有效地将弱目标与重叠的巨大干扰分开。实验结果表明，我们提出的模型具有较强的抗干扰性、较低的检测损失和短期积累性，明显优于传统的后验概率模型。