Shape-driven multiple extended target tracking and classification based on B-Spline and PHD filter

Multiple extended target tracking based on distributed multi‐sensor fusion and shape estimation

“…The shape similarity essentially depends on the corresponding peaks, which requires a matching strategy to match the peaks of the two shapes. The cycle shifting method is selected as the matching strategy, which is referred to [20].…”

“…For better estimation of the similarity of shapes, the 20 polar radii can be extended to 360 polar radii, for example, fρg 20 i¼1 → fρg 360 i¼1 . The likelihood of shapes X 1 and X 2 can be calculated by…”

“…match the peaks of the two shapes. The cycle shifting method is selected as the matching strategy, which is referred to [20].…”

“…Moreover, the shape features cannot be fully utilised for state update to improve the tracking accuracy, especially for the scenario with closely spaced METs. To address these problems, we have proposed a Shape-driven METT and a classification algorithm in previous work [20]. In this paper, we will introduce the association technique for identifying the newborn targets and reducing the computational burden on the basis of the previous work and extend it to the distributed multi-sensor fusion framework for further enhancing the tracking accuracy of METT.…”