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

Author

Yanhe Cheng ; Wenge Yang ; Jiang Zhao

Author_Institution

Dept. of Opt. & Electr. Equip., Equip. Acad., Beijing, China

fYear

2014

fDate

19-23 Oct. 2014

Firstpage

1665

Lastpage

1670

Abstract

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.

Keywords

aerospace control; compressed sensing; radio tracking; radiotelemetry; TT&C signal sparsity; basic learning dictionary; compressive sensing; high speed sampling pressure; noise reduction; signal feature; telemetry tracking and control; two-stage dictionary learning; Algorithm design and analysis; Dictionaries; Frequency-domain analysis; Matching pursuit algorithms; Noise; Time-domain analysis; Vectors; DS TT&C signal; basic learning dictionary; sparsity; two-stage dictionary learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing (ICSP), 2014 12th International Conference on

Conference_Location

Hangzhou

ISSN

2164-5221

Print_ISBN

978-1-4799-2188-1

Type

conf

DOI

10.1109/ICOSP.2014.7015278

Filename

7015278