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Source Localization Via Randomly Distributed Sensors

Start: 3/2/2017 at 2:00PM
End: 3/2/2017 at 3:00PM
Location: 258 Fitzpatrick Hall
Event Url: http://www.ee.nd.edu/seminars/source-localization-via-randomly-distributed-sensors#sthash.AT1Eq26h.dpuf
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Seminar Speaker:

Itsik Bergel, Bar-Ilan University

Itsik Bergel received the B.Sc. in electrical engineering and the B.Sc. in physics from Ben Gurion University, Beer-Sheva, Israel, in 1993 and 1994, respectively, and the M.Sc. and Ph.D. in electrical engineering from the University of Tel Aviv, Tel-Aviv, Israel, in 2000 and 2005 respectively. From 2001 to 2003, he was a Senior Researcher at INTEL Communications Research Lab. In 2005, he was a Postdoctoral researcher at the Dipartimento di Elettronica of Politecnico di Torino, Italy, working on the capacity of non-coherent channels. In 2004 he received the Yitzhak and Chaya Weinstein study award. He is a faculty member in the faculty of engineering at Bar-Ilan University, Ramat-Gan, Israel.

His main research interests include multichannel interference mitigation in wireline and wireless communications, cooperative transmission in cellular networks and cross layer optimization of random ad-hoc networks.

Seminar Description

Source localization is a well-studied problem, where the location of a transmitting source is estimated from the signals received in multiple sensors. However, all results on the accuracy of source localizations are a function of the specific sensor locations. We present novel lower bounds on the mean-square source-localization error via a network where the sensors are deployed randomly (e.g., scattered from the air). The sensor locations are modeled as a homogenous Poisson point process. We present CRB-type bounds on the expectation of the localization square error, which are not a function of a particular sensor configuration, but rather of the sensor statistics. Thus, it can be evaluated prior to the sensor deployment and provide insights into design issues such as the necessary sensor density. The derived bounds are simple for evaluation while providing a good prediction of the actual network performance.