UP	Paper 1664	US-W-G	DOWN
Global Optimization for Multiple Transmitter Localization
Nelson,Jill		George Mason University
Hazen,Megan		University of Washington
Gupta,Maya		University of Washington
We propose a global optimization approach to locating multiple transmitters within a geographic area. A set of sensor nodes are assumed to be present in the region and to measure total power received at their respective locations. These measurements are communicated to a processing node, which uses particle swarm optimization to find the transmitter locations that minimize the difference between the true received power and the estimated power based on the chosen propagation model. Clustering is used to generate initial estimates of the transmitter locations, thereby increasing the likelihood that the particle-based optimizer reaches the global minimum. Simulation results show that global optimization is an effective method for multiple transmitter localization and that generating &096;&096;smart'' initial conditions via clustering can yield an average performance improvement of over 25\% compared to random initial conditions.
Jill Nelson received a B.S. in Electrical Engineering and a B.A. in Economics from Rice University. She received an M.S. and a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. She has been an Assistant Professor of Electrical and Computer Engineering at George Mason University since the fall of 2005. Dr. Nelson’s research lies in statistical signal processing and signal processing for communications. Specifically, her interests include equalization and coding for dispersive channels, iterative detection and decoding, blind equalization, and cooperative detection in multi-user communications.