Abstract
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.
Highlights
Most research concerning automatic detection and tracking technology is focused on ground objects
Comparing the multiple targetsthe tracking diagram correlation and the actual and predicted trajectory comparison diagram three disparate disparate algorithms, found that the the comparison obtained from the three correlation algorithms, we found that comparison diagram obtained from theaccomplish three disparate correlation algorithms, we found that the algorithm can nearly the tracking of target when the two targets are joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithm can nearly accomplish when the two targets are algorithm can nearly accomplish the tracking of target when the two targets are cross-moving
The mechanically scanned imaging sonar is fixedly mounted on autonomous underwater vehicles (AUVs) for underwater targets tracking
Summary
Most research concerning automatic detection and tracking technology is focused on ground objects. Underwater multi-target detection and tracking are in great demand. The past decade has witnessed the fast development of ocean observation, which has been a success in the application of subaqueous robots for underwater detection and salvage, target recognition, and location tracking [1,2,3,4]. The special underwater operation environment has been a hindrance to the development of marine resources, requiring professional underwater tools. As a sort of subaqueous operation device, autonomous underwater vehicles (AUVs) can complete underwater operations under the condition of unmanned operation, and can fulfill the tasks of environmental detection, target recognition, and so on [9,10,11]. With the continuous development of related technologies and theories in recent decades, AUV technology is constantly upgrading, and its application to complete underwater detection is low-cost and highly-efficient. The utilization of AUVs is more extensive in tasks such as marine resources exploration and marine environment survey [12,13,14]
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