摘要：Because of their volume and power limitation, it is difficult for CubeSats to configure a traditional propulsion system. Atmospheric drag is one of the space environmental forces that low-orbit satellites can use to realize orbit adjustment. This paper presents an integrated control strategy to achieve the desired in-track formation through the atmospheric drag difference, which will be used on ZJUCubeSat, the next pico-satellite of Zhejiang University and one of the participants of the international QB50 project. The primary mission of the QB50 project is to explore the near-Earth thermosphere and ionosphere at the orbital height of 90-300 km. Atmospheric drag cannot be ignored and has a major impact on both attitude and orbit of the satellite at this low orbital height. We conduct aerodynamics analysis and design a multidimensional nonlinear constraint programming （MNLP） strategy to calculate different desired area-mass ratios and corresponding hold times for orbit adjustment, taking both the semimajor axis and eccentricity into account. In addition, area-mass ratio adjustment is achieved by pitch attitude maneuver without any deployable mechanism or corresponding control. Numerical simulation based on ZJUCubeSat verifies the feasibility and advantage of this design.

摘要：Robust object tracking has been an important and challenging research area in the field of computer vision for decades. With the increasing popularity of affordable depth sensors, range data is widely used in visual tracking for its ability to provide robustness to varying illumination and occlusions. In this paper, a novel RGBD and sparse learning based tracker is proposed. The range data is integrated into the sparse learning framework in three respects. First, an extra depth view is added to the color image based visual features as an independent view for robust appearance modeling. Then, a special occlusion template set is designed to replenish the existing dictionary for handling various occlusion conditions. Finally, a depth-based occlusion detection method is proposed to efficiently determine an accurate time for the template update. Extensive experiments on both KITTI and Princeton data sets demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse learning and RGBD based methods.