In order to optimize the design of satellite constellations with specific area coverage and intensive revisits, a design scheme of regression orbit and co-subsatellite point trajectory constellation was adopted, and a method to optimally solve the satellite constellation orbit parameters by integrating genetic ant colony algorithm with the weight ranking coverage of key areas in specific area was proposed. The design requirements of the regional coverage constellation were analyzed, a regression orbit coverage area model was established, the optimal orbit root number was calculated by using the genetic ant colony algorithm, and all the constellation parameters were solved through using the co-subsatellite point trajectory constellation solution algorithm. The simulation results show that the optimized constellation meets the coverage time and revisit frequency requirements for the area target, and provides focused coverage and revisits of important locations in order of weight, which verifies the feasibility of the algorithm.

To ensure the safety of the space station and improve the accuracy of the estimated trajectory tracking of noncooperative target, an optical imaging micro-/nanosatellite based on APSO-SSUKF (adaptive particle swarm optimization-spherical simplex unscented Kalman filter) is proposed to track low-orbit target using angle-only measurement. First, the algorithm considers the effect of J2 perturbation, uses the angle-only data as the observation vector, and uses spherical simplex unscented Kalman filter (SSUKF) to reduce the cost of calculation of the UKF in space noncooperative target tracking. Secondly, it is proposed to use the actual and theoretical covariance of the innovation sequence for real-time estimation of measurement noise, designing the adaptive particle swarm optimization (APSO) algorithm for real-time tracking of the process noise in the SSUKF that improves the accuracy of the filter in angle-only tracking. Finally, the tracking simulation of low-orbit satellite is carried out by using optical imaging micro-/nanosatellite, and the result shows that, compared with UKF, SSUKF, and PSO-SSUKF, APSO-SSUKF reduces the root mean square of the error in predicting the position in space target tracking by 45.44%, 35.26%, and 20.94%, and APSO-SSUKF reduces the root mean square of the error in velocity by 45.58%, 33.53%, and 16.33%, respectively; in the angle-tracking target, APSO-SSUKF improves the convergence and estimated accuracy of the algorithm in tracking.