Remote Sensing Satellites Planning and Scheduling Based on the Improved Particle Swarm Optimization Algorithm

With the development of aerospace technology, both the number of remote sensing satellites and observation tasks have increased rapidly, which promotes research on remote sensing satellites planning and scheduling, and presents more complex requirements and challenges for research on remote sensing satellites planning and scheduling. Based on existing related research, this paper proposed the basic assumptions and simplifications of remote sensing satellites mission. Applying the analytical form of the instantaneous limit detection range of a satellite with push-broom scanning payload, the satellite’s access to regional targets was analysed and calculated. Using the centre projection transformation, the regional targets were clipped according to the satellite’s push-broom scanning strips. The satellite’s reachable domain to regional targets was calculated, and the remote sensing satellite’s coverage percentage of polygon region targets was obtained based on the derivation results. After that, a mathematical model for remote sensing satellites planning and scheduling was established, in which some mission constraints were expressed in mathematical terms. And a model solution framework was established based on the Particle Swarm Optimization (PSO) algorithm. The initial feasible solution generated by the greedy algorithm and resampling was introduced into the solution process. Finally, the mathematical model and solution algorithm of remote sensing satellites planning and scheduling designed in this paper was applied to a simulation example, and good scheduling results were obtained to verify the effectiveness and practicality of the relevant mathematical model and solution algorithm.