Research of TT&C signal sparsity based on two-stage dictionary learning

The Broadband is a notable trend of the TT&C system, which will be certain to lead to high speed sampling pressure and massive data problem. Theory of compressive sensing can solve the issue. However, signal sparsity is an important prerequisite for compressive sensing. On basis of the dictionary learning, the sparsity of DS TT&C signal was studied preliminarily. Through in-depth analysis of dictionary learning algorithms, a two-stage dictionary learning algorithm is provided that is combined with the DS TT&C signal feature, and the basic learning dictionary can be got. Then the performance of the sparse representation for the DS TT&C signal is studied by the simulation experiment. The results of simulation show that DS TT&C signal can get a strong sparsity in basic learning dictionary, which has some noise reduction performance.