Signal Reconstruction from Sparse Measurements Using Compressive Sensing Technique
The paper presents the possibility of applying a new class of mathematical methods, known as Compressive Sensing (CS) for recovering the signal from a small set of measured samples. CS allows the faithful reconstruction of the original signal back from fewer random measurements by making use of some non-linear reconstruction techniques. Since of all these features, CS finds its applications especially in the areas where, sensing is time consuming or power constrained. An electromagnetic interference measurement is a field where the CS technique can be used. In this case, a sparse signal decomposition based on matching pursuit (MP) algorithm, which decomposes a signal into a linear expansion of element chirplet functions selected from a complete and redundant time-frequency dictionary is applied. The presented paper describes both the fundamentals of CS and how to implement MP for CS reconstruction in relation to non-stationary signals.
KeywordsCompressive sensing Matching pursuit
- 10.Baraniuk, R.: An Introduction to compressive sensing. http://legacy.cnx.org/content/col11133/1.5/. Accessed 21 May 2018
- 12.Yoo, J., Becker, S., Monge, M., Loh, M., Candès, E.J., Emami-Neyestanak, A.: Design and implementation of a fully integrated compressed-sensing signal acquisition system. In: Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5325–5328. IEEE, Kyoto (2012)Google Scholar
- 15.Wang, Z., Lee, I.: Sorted random matrix for orthogonal matching pursuit. In: International Conference on Digital Image Computing: Techniques and Applications, pp. 116–120. NSW, Sydney (2010)Google Scholar