An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking

Abstract

To obtain accurate underwater target tracking results, an improved kernelized correlation filter (IKCF) algorithm is proposed to track the target in forward-looking sonar image sequences. Specifically, a base sample with a dynamically continuous scale is first applied to solve the poor performance of fixed-scale filters. Then, in order to prevent the filter from drifting when the target disappears and appears again, an adaptive filter update strategy with the peak to sidelobe ratio (PSR) of the response diagram is developed to solve the following target tracking errors. Finally, the experimental results show that the proposed IKCF can obtain accurate tracking results for the underwater targets. Compared to other algorithms, the proposed IKCF has obvious superiority and effectiveness.