基于SAS数据的水下复杂场景中目标识别研究

项目名称： 基于SAS数据的水下复杂场景中目标识别研究

项目编号： No.11204343

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 物理学I

项目作者： 刘维

作者单位： 中国科学院声学研究所

项目金额： 30万元

中文摘要： 水下复杂场景中目标识别是水下无人平台（Underwater Unmanned Vehicle,UUV）完成自主探测任务的关键。合成孔径声纳（Synthetic Aperture Sonar,SAS）可提供目标高分辨率声图和多视角回波两类数据，被认为是UUV完成水下小目标探测的最佳载荷，基于SAS数据的目标识别具有重要的科学意义和迫切的应用需求。该课题旨在建立基于两类SAS数据的水下目标识别方法体系，为UUV自主探测提供关键技术支撑。目标声图处理方面，引入SAS成像模型、水声物理模型和图像统计模型，研究水下复杂场景中目标特征区域（亮区和阴影区）的自动提取方法。在此基础上，以目标声图为先验知识，利用SAS成像模型精确提取目标多视角回波数据，并采用海豚耳蜗模型将回波数据转换为三维特征图。采用基于模型的方法以声图特征为主完成目标识别，以目标三维特征图为辅完成人工目标和非人工目标识别。

中文关键词： 合成孔径声纳；水下无人平台；目标检测识别；管线检测跟踪；海豚仿生

英文摘要： Underwater target recognition in complicated seafloor circumstances is pivotal for UUV(Underwater Unmanned Vehicle) to perform the autonomous surveillance task. SAS(Synthetic Aperture Sonar) is capable of providing high resolution acoustic images and multi-aspect echo data and is considered as the deployment especially suitable for UUV and underwater small target detection. Target recognition based on SAS data is scientifically significant and of urgent demand. The object of the proposed project is to develop methods related with underwater target recognition based on the two-class SAS data and to provide a support for the UUV autonomous surveillance task. On SAS image processing, models including SAS imaging, underwater acoustics and image statistics are fused with the target recognition methods. Firstly, the recognition methods are focused on the automatic segmentation of target highlight and shadow region in complicated seafloor circumstances. After that, the target highlight is utilized as a priori knowledge to pick up the target's multi-aspect echo data with SAS imaging model. The dolphin cochlear model is applied to the echo data and produce the target's 3-D feature image. The height and shadow are utilized as primary information by the model-based method for recognition. The target's 3-D feature image are

英文关键词： Synthetic Aperture Sonar；Underwater Unmanned Vehicle；Automatic Target Detection and Recognition；Oil Pipeline Detection and Tracking；Dolphin Bio-Sonar