Compressão de sinais por códigos chirp

Abstract

Compressive sensing is a technique that uses the sparseness of signals in some dimension, to process them while they are compressed using fewer samples than techniques such as Shannon-Nyquist, allowing less memory to be used, making cheaper hardware. The method used in this work is focused on the use of deterministic matrices made with chirp codes to process signals with harmonics and reconstruct them, the technique is demonstrated in this work, its limitations and the results of its application in harmonic signals, an analysis was made of the cases in which it brings good results and situations in which its use is not recommended. It is seen that the technique has its implementation particularities, but within the requirements it fulfills the proposed objective, recovering the signal with few samples with a relatively low error, thus enabling the reduction of data storage costs.