The point target assumption, which suggests that a target can generate at most one measurement at a time, is used in typical target tracking algorithms. However, in many practical applications, multiple scattering poi...
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The point target assumption, which suggests that a target can generate at most one measurement at a time, is used in typical target tracking algorithms. However, in many practical applications, multiple scattering points of a target can be resolved using a high-resolution sensor, which gives rise to the multipledetectionproblem. The typical algorithms with the point target assumption are not eligible for multipledetectiontracking environments. The multipledetection joint integrated probabilistic data association algorithm is designed to solve the multiple detection multitarget tracking problem. However, the computational complexity of this algorithm grows exponentially with the number of tracks and measurement cells. Here, multipledetection linear multitarget integrated probabilistic data association is proposed to enhance computational efficiency by introducing the modulated clutter measurement density, which takes into account the contributions of clutter as well as other targets of each measurement cell. The computational complexity of the proposed algorithm is linear in the number of tracks and the number of measurement cells. Simulation results verify the applicability and efficiency of the proposed algorithm in multipledetectionmultitargettracking scenarios.
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