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Algorithm Performance Analysis for Reconstruction of Sparse Time-Frequency Distributions from Compressive Sensed Ambiguity Function


Volarić, Ivan; Sučić, Viktor; Jurdana, Irena
Algorithm Performance Analysis for Reconstruction of Sparse Time-Frequency Distributions from Compressive Sensed Ambiguity Function // Proceedings of International Conference on Innovative Technologies IN-TECH 2015 / Car, Zlatan ; Kudlaček, Jan (ur.).
Rijeka: Faculty of Engineering University of Rijeka, 2015. str. 26-29 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Algorithm Performance Analysis for Reconstruction of Sparse Time-Frequency Distributions from Compressive Sensed Ambiguity Function

Autori
Volarić, Ivan ; Sučić, Viktor ; Jurdana, Irena

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of International Conference on Innovative Technologies IN-TECH 2015 / Car, Zlatan ; Kudlaček, Jan - Rijeka : Faculty of Engineering University of Rijeka, 2015, 26-29

Skup
International Conference on Innovative Technologies IN-TECH 2015

Mjesto i datum
Dubrovnik, Hrvatska, 09.-11.09.2015

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Time-frequency representation; Instantaneous frequency estimation; Signal sparsity; Compressive sensing; Basis pursuit; Linear unconstrained optimization.

Sažetak
Time-frequency distributions (TFDs) are powerful tools for analysis of nonstationary signals, which allow us to observe signal energy distribution as a function of both time and frequency simultaneously. However, TFDs are often corrupted with artifacts, a mathematical byproduct of TFD quadratic nature. One method for their elimination is compressive sensing (CS) of signal ambiguity function (AF) in such a way to discard corrupted samples, leaving only a small subset of artifact-free samples. In order to obtain a high-resolution TFD, one needs to solve an optimization problem, namely the basis pursuit. In this paper, we perform a comparison of existing algorithms for solving unconstrained optimization problems, adapted for reconstruction of a TFD from a small number of CS-AF samples.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA


Ustanove
Tehnički fakultet, Rijeka