The problem of super-resolution and tracking for midcourse closely spaced objects (CSO) is examined using a space-based infrared sensor. Within a short time window, the midcourse CSO trajectories on the focal plane can be modeled as following a straight line with a constant velocity. Thus, the object's initial state (location and velocity on the focal plane) exclusively corresponds to its trajectory on the focal plane. Thereupon, the objects number, intensities and initial states, as well as the sensor noise variances, are considered random variables, and a Bayesian model is proposed which is utilized to define a posterior distribution on the joint parameter space. To maximize this distribution, reversible jump Markov chain Monte Carlo algorithm is adopted to perform the Bayesian computation. The proposed approach simultaneously used the multiple time-consecutive frame data to estimate model parameters. Compared with the single-frame method, it not only gains the super-resolution capability but also can directly estimate focal plane trajectories without using explicit data association techniques. Results show that the performance (estimation precision of objects number, focal plane locations, intensities and ballistic trajectories for the CSO, together with the computation load) of the proposed approach outperforms the conventional single-frame and multiframe approaches.