Low earth orbit (LEO) satellite systems, an important part of the next generation of global communication systems, have the advantages of low transmission delay, low satellite cost and low launch cost. The construction of an LEO satellite network with global coverage has become the direction of future space network transmission development. Although extensive research has been conducted on the routing of LEO satellite networks, most papers focus on only space segment routing, with little attention paid to the route between the satellite and ground station. This paper introduces the transmission scenario of ground station switching with connected satellites and analyzes the problem of data packet loss caused by ground station and satellite communication link switching. Two optimization strategies based on static routing and dynamic routing are proposed as solutions to the problem of data packet loss, with software-simulated test results showing that both approaches can effectively avoid packet loss.

An image deconvolution approach based on regularized sparse representation is proposed. Given an observed blurred image, traditional deconvolution approach based on sparse representation constructs the 1 -norm regularization directly by using the direct observed model and the sparsity of image decomposed on a redundant dictionary. However, the 1 -norm regularization has large coherence because of the compactness of the blurring operator. Therefore, we multiply the blurred image with a regularizing operator and construct the 1 -norm regularization by using the converted data. The approach, referred as regularized sparse representation, possesses the following merits: (i) it decreases the coherence of the 1 -norm regularization; (ii) algorithm solving the regularization is fast converged due to the preconditioning strategy. Experimental results demonstrate that comparing with existed state-of-the-art approaches, the proposed approach can obtain good results in terms of speed and deconvolution performance.