Efficient and accurate feature-aided active tracking for underwater small targets in highly cluttered harbor...

To address the issue of tracking highly maneuverable small underwater intruders in the dynamic and heavily interfered environment of harbors, a local sparse motion acoustic flow (LSMAF) computation method constrained by robust high-order flux tensor (RHO-FT) feature has been proposed, along with a LSMAF feature-aided active tracking method. First, potential targets are localized from the dense dynamic clutter background of active sonar echographs using RHO-FT feature maps. Subsequently, on the basis of a dense motion acoustic flow calculation method, a local motion consistency criterion is introduced to the potential target area, establishing a method for calculating LSMAF, which updates the precise motion vectors of potential targets in real time. Finally, all measurements obtained from the RHO-FT feature map are iteratively filtered together with their corresponding motion vectors to maintain the kinematic consistency of the potential target's trajectory. Experiments conducted on a series of cooperative targets in real-world harbor environments show that LSMAF-aided tracking method outperforms the latest developments of tracking for underwater targets in terms of both tracking efficiency and trajectory accuracy.