Efficient and accurate feature-aided active tracking for underwater small targets in highly cluttered harbor environments using a full motion acoustic flow field solution.

Abstract

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.