Underwater acoustic target detection in remote marine sensing operations is challenging due to complex sound wave propagation. Despite the availability of reliable sonar systems, target recognition remains a difficult problem. Various methods address improved target recognition. However, most struggle to disentangle the high-dimensional, non-linear patterns in the observed target recordings. In this work, a novel method combines a time delay neural network and histogram layer to incorporate statistical contexts for improved feature learning and underwater acoustic target classification. The proposed method outperforms the baseline model, demonstrating the utility in incorporating statistical contexts for passive sonar target recognition. The code for this work is publicly available.

翻译：在水下远程海洋传感作业中，由于复杂声波传播特性，水声目标检测极具挑战性。尽管已有可靠声纳系统可用，目标识别仍是一个难题。现有多种方法致力于提升目标识别性能，但大多难以有效解构观测目标记录中高维非线性模式。本文提出一种融合时延神经网络与直方图层的新方法，通过引入统计上下文信息增强特征学习能力，从而实现水下声学目标分类。实验表明，该方法在被动声纳目标识别任务中显著优于基线模型，验证了统计上下文信息融入的有效性。本工作相关代码已开源。