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In this paper, we propose an extension of the soft-gating approach for measurement-to-target assignment for multitarget tracking. Given the latest observation and a set of multitarget particles, the proposed method combines efficient m-best 2D data assignment and sampling methods to compute a feasible measurement-to-target assignment with an associated probability for each particle. The particles containing the multitarget states and the association vectors can then be used to recursively estimate the posterior distribution of the targets using sequential Monte Carlo methods. Computer simulations demonstrate the robustness and effectiveness of the proposed method for data association and multitarget tracking.