Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Nantong Street 145, Harbin 150001, China.
Sensors (Basel). 2019 Jan 17;19(2):370. doi: 10.3390/s19020370.
Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.
自主水下航行器（AUV）依靠机械扫描成像声纳进行水下目标避障和跟踪，该声纳固定安装在 AUV 上。当水下目标交叉或接近时，由于多目标的不正确关联，AUV 有时无法跟踪或跟踪错误的目标。因此，提出了一种采用云状模型数据关联算法的跟踪方法，以便跟踪水下多个目标。聚类云状模型（CCM）不仅结合了定性概念的模糊性和随机性，而且实现了定量值的转换。此外，最近邻算法也参与寻找与每个目标轨迹相对应的聚类中心，并且提出了 AUV 的硬件架构。在 AUV 上固定安装机械扫描成像声纳进行了海上试验，以验证所提出算法的有效性。实验结果表明，与联合概率数据关联（JPDA）和近邻数据关联（NNDA）算法相比，新算法具有更准确聚类的特点。
