The joint integrated probabilistic data association (JIPDA) filter is effective for automatic multi-target tracking in cluttered environments. However, it is well-known that when targets are closely spaced, the JIPDA filter encounters the track coalescence problem, leading to inaccurate state estimations. This paper proposes a novel particle swarm optimization-based JIPDA (PSO-JIPDA) algorithm, which improves the state estimation accuracy by optimizing the posterior probability density, effectively addressing the information fusion challenge in multi-target tracking scenarios with closely spaced targets. The trace of the covariance matrix of the posterior density serves as the objective function for the optimization problem. Minimizing the trace enhances the accuracy of target state estimation by refining the posterior density. Specifically, all possible permutations of the targets are enumerated, with each permutation assigned a unique index. These indices are mapped to association hypothesis events within a probabilistic fusion framework, where each mapping corresponds to a particle in the PSO algorithm. The particles are initialized by stochastically assigning indices to hypothesis events, forming the initial swarm. During iterations, the particles dynamically adjust their positions and velocities based on individual and global optimal solutions, guided by the trace minimization objective. Experimental results demonstrate that the PSO-JIPDA algorithm significantly improves the accuracy of Gaussian approximation and makes notable progress in addressing the track coalescence problem.