Underwater Multitarget Tracking Method Based on Threshold Segmentation

The increasing development of underwater recording and processing systems has created a demand for automated methods that accurately detect and track small underwater targets in sonar images. However, precisely tracking such targets in underwater environments remains challenging. This article proposes an improved automated underwater multitarget tracking method integrating threshold segmentation with the Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) algorithm. Using the GM-CPHD algorithm instead of the Gaussian mixture probability hypothesis density (GM-PHD) helps mitigate estimation errors in target numbers, leading to improved accuracy in position estimation. In addition, to address the issue of missed detection, a secondary detection process is incorporated into the method, leading to a reduction in the missed detection rate. By considering the target area feature, the proposed method automatically sets an area threshold to distinguish between real targets and clutter, thereby reducing the average optimal subpattern assignment (OSPA) distance and minimizing the number of target trajectory breaks. The effectiveness of the proposed method is demonstrated through simulations and experimental tracking results. This method shows accurate tracking of multiple underwater targets, improved robustness, and holds potential for practical applications.