具身认知声呐探测技术研究  

Research on embodied cognitive for sonar detection technology

作　　者：潘悦 吴玺宏[2] 王强 曹怀刚 曲天书[2] PAN Yue;WU Xihong;WANG Qiang;CAO Huaigang;QU Tianshu(System Demonstration Research Center of Applied Acoustic Research Institute,Key Laboratory of Sonar Technology,Beijing 100089,China;State Key Laboratory of General Artificial Intelligence,School of Intelligence Science and Technology,Peking University,Beijing 100871,China)

机构地区：[1]杭州应用声学研究所体系论证研究中心,声呐技术重点实验室,北京100089 [2]北京大学智能学院,跨媒体通用人工智能全国重点实验室,北京100871

出　　处：《声学技术》2025年第1期1-12,共12页Technical Acoustics

摘　　要：文章针对智能声呐探测中面临的水声数据有效样本稀疏和探测结果可解释性差等基础性问题,揭示了壳体声呐能够利用“身体”增强感知的机理,给出了闭环自学习的水声智能探测通用范式,为声呐设计提供新原理、新方法支撑。相较于传统声呐,文章所提出的具身认知声呐探测技术具有增强感知和自学习能力。通过实际试验数据验证,以物理模型为驱动构建的具身认知模型在检测、测向和定位等任务中均体现出显著优势。新方法对水声数据量的需求小且能够显著提高目标探测能力,为解决当前人工智能方法在声呐应用中遇到的问题奠定了基础,可广泛应用于壳体声呐。Aiming at the core issues of sparse effective samples and poor interpretability of detection results in intelligent sonar systems,an embodied cognition modeling approaches driven by physical model is presented to reveal the mechanism by which the hull-mounted sonar can exploit its"body"to enhance perception,and a comprehensive framework for closed-loop self-learning underwater intelligent detection is introduced to offer new principles and methods for sonar design.Through the validation of experimental data,the proposed method demonstrates significant advantages in source detection,azimuth estimation and localization compared to the traditional methods.Embodied cognition model requires less underwater acoustic data and enhances source detection capability significantly,which lays a foundation for addressing the challenges faced by current artificial intelligence methods in sonar applications and enabling broad application in hull-mounted sonar.

关 键 词：壳体声呐 具身认知 目标探测 阵列信号处理 

分 类 号：TB566[交通运输工程—水声工程]