With the development of large low earth orbit (LEO) communication constellations, the efficiency of laser inter-satellite link (ISL) establishing become the bottleneck for subsequent large-scale launch and rapid networking applications of LEO communication constellations. Hence, we establish the pointing jitter error structure of LEO communication experiment satellites (LCES) system. The error structure can be used to trace the source of errors and evaluate the in-orbit jitter. And we derive an analytical expression of the acquisition probability density function (PDF) which comprehensively considering the influence of the scanning region, the pointing jitter error, the overlap factor and the in-orbit jitter error. The multi-parameter influenced acquisition model is validated by Monte Carlo (MC) simulations and semi-physical tests. The results reveals that the multi-parameter influenced acquisition model can be used to guide the in-orbit ISL establishing.

Long-term monitoring of satellite microvibrations generates a significant amount of data streams, placing strain on satellites with limited transmission capacity. To relieve this transmission strain, a dynamic compressed sensing (CS) framework is proposed for satellite microvibration measurements. Microvibration streams are measured block by block and then reconstructed by a dynamic recovery algorithm. The recovery solution of one block can be used as a priori knowledge for the next block, allowing for faster updates. However, the existing dynamic recovery algorithms are only applicable to the real domain and cannot be applied to the microvibrations projected on a Fourier basis in the complex domain. In light of this, the dynamic homotopy algorithm is expanded to the complex domain to deal with microvibration signals that are sparse on the Fourier basis. In comparison to the traditional uniform sampling methods, the experimental results show that the dynamic CS with the expanded recovery algorithm can achieve a maximum root-mean-square acceleration (Grms) deviation of 4% in power spectrum density (PSD) with 1/5 sampling points. Compared with recovery algorithms applicable to fixed measurements, the dynamic algorithm can achieve comparable accuracy in about 1/3 of computation time. The experimental results demonstrate the feasibility of satellite microvibrations measurement using dynamic CS.