The closely spaced objects (CSOs) create blur imprints on infrared focal plane,which make it necessary for information processing to have a super-resolution. Objects′ imaging on infrared focal plane is modeled,and a super-sesolution objective function based on least square criterion is presented. Traditional optimization methods have to be carefully initialized otherwise they will get poor estimation performance and suffer from large computation load. So a particle swarm optimization (PSO) algorithm is introduced to optimize the super-resolution objective function,and jointly estimate the projection position and radiant intensity of targets on the focal plane,then realize the super-resolution of CSOs. Simulation results show that the least square-based PSO algorithm gains superior performances than that of the traditional steepest descent method and possesses the better capability of super-resolution.