QPSO-based algorithm of CSO joint infrared super-resolution and trajectory estimation

The midcourse ballistic closely spaced objects (CSO) create blur pixel-cluster on the space-based infrared focal plane, making the super-resolution of CSO quite necessary. A novel algorithm of CSO joint super-resolution and trajectory estimation is presented. The algorithm combines the focal plane CSO dynamics and radiation models, proposes a novel least square objective function from the space and time information, where CSO radiant intensity is excluded and initial dynamics (position and velocity) are chosen as the model parameters. Subsequently, the quantum-behaved particle swarm optimization (QPSO) is adopted to optimize the objective function to estimate model parameters, and then CSO focal plane trajectories and radiant intensities are computed. Meanwhile, the estimated CSO focal plane trajectories from multiple space-based infrared focal planes are associated and filtered to estimate the CSO stereo ballistic trajectories. Finally, the performance (CSO estimation precision of the focal plane coordinates, radiant intensities, and stereo ballistic trajectories, together with the computation load) of the algorithm is tested, and the results show that the algorithm is effective and feasible.