Conventional multi-target tracking (MTT) algorithms often struggle with scenarios governed by strong structural constraints, e.g.,exploiting integer programming solving the probabilistic objective is very computation expensive or intractable. This letter addresses the one-to-one association constraint, which is particularly relevant in high-fidelity tracking scenarios operating under a high detection-probability (PD) assumption, such as those with high signal-to-noise-ratio (SNR) sensor arrays. We propose a novel strong association constraint EM (SAC-EM) algorithm that embeds this hard constraint within a principled expectation-maximization (EM) framework. The E-step elegantly formulates the constrained data association as an entropic regularized optimal transport problem, which can be solved efficiently by the iterative proportional fitting (IPF) algorithm. The M-step then reduces to a weighted Kalman filter update. Comparative simulations demonstrate that the proposed algorithm achieves superior tracking accuracy over relevant baselines in the scenarios with constraints context, validating the EM framework as a powerful tool for integrating hard combinatorial constraints into probabilistic estimation.