Document Type


Date of Award

Spring 5-31-2019

Degree Name

Master of Science in Electrical Engineering - (M.S.)


Electrical and Computer Engineering

First Advisor

Alexander Haimovich

Second Advisor

Ali Abdi

Third Advisor

Joerg Kliewer


Distributed sensors observe radio frequency (RF) sources over flat-fading channels. The activity pattern is sparse and intermittent in the sense that while the number of latent sources may be larger than the number of sensors, only a few of them may be active at any particular time instant. It is further assumed that the source activity is modeled by a Hidden Markov Model. In previous work, the Blind Source Separation (BSS) problem solved for stationary channels using Dictionary Learning (DL). This thesis studies the effect of time-varying channels on the performance of DL algorithms. The performance metric is the probability of detection, where a correct detection is the event that the estimated value of a source exceeds a threshold at a time instant when the true source is active. Using the probability of detection when the channels are stationary as a baseline, it is shown that there is significant degradation for time-varying channels and observation intervals much longer than the time coherence. Detection performance improves when the observation time is approximately equal to the time coherence. Performance is again degraded when the observation is shorter and there is not sufficient information for the DL algorithms to learn from.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.