Naslov
Signal Concentration Enhancement in the Time-Frequency Domain Using Adaptive Compressive Sensing

Autori
Volarić, Ivan

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, doktorska disertacija

Fakultet
Tehnički fakultet

Mjesto
Rijeka

Datum
04.09

Godina
2017

Stranica
186

Mentor
Sučić, Viktor

Ključne riječi
Time-frequency signal representation, Ambiguity function, Cross-terms suppression, Signal sparsity, Compressive sensing, Linear unconstrained optimization, Intersection of confidence intervals method, Rényi entropy.

Sažetak
Signals with the time-varying frequency content are best represented in the joint time-frequency domain, with the components instantaneous frequency laws being their key non-stationary features. However, the most commonly used methods for the time-frequency distribution (TFD) calculation generate unwanted artifacts, making their interpretation more difficult. In order to overcome this limitation, a number of methods have been proposed utilizing the compressive sensing (CS) for the artifact removal, while the unavoidable resolution loss is reduced by the signal reconstruction algorithm which, in its core, solves a linear unconstrained optimization problem. The performance of the existing methodology relies primarily on the user-predefined parameters, namely the CS area and input parameters of the sparse reconstruction algorithm, which are in most cases chosen experimentally. This approach has resulted in the unreliability of the sparse TFD methods, as the parameters which perform well for one signal will not necessarily perform equally for a different signal. In order to overcome this problem, three adaptive methods are proposed in this thesis, jointly resulting an an adaptive data-driven solution. The first proposed algorithm, adaptively detects the CS area, while the remaining two algorithms are adaptive sparse reconstruction algorithms based on the intersection of confidence intervals rule and the localized Rényi entropy, respectively. The proposed adaptive sparse reconstruction algorithms can be used in the conjunction with the adaptive CS area selection method in order to increase the concentration of the resulting sparse TFD even further. The here proposed methods are tested on synthetical and real-life signals, and the obtained results are compared with the results obtained with the currently available state-of-the-art sparse TFD reconstruction methods.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika

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