Department of Mathematical Sciences at Korea Advanced Institute of Science and Technology (KAIST)

Biography: Sung-Ho Kim is a Professor of Statistics in the Department of Mathematical Sciences at Korea Advanced Institute of Science and Technology (KAIST). He received his BA degree from Seoul National University and MA and PhD degrees in Statistics from Carnegie Mellon University. He held a position as a research scientist at Educational Testing Service (ETS), Princeton, from 1989 to 1993, where he worked on statistical modeling for problem solving. Dr. Kim has taught Statistics and Mathematics at KAIST since he joined the university in 1993 and has published over 50 research articles. The journals where his articles have appeared include Journal of the American Statistical Association, Computational Statistics and Data Analysis, Biometrika, Decision Support Systems, J. of Neuroscience Methods, and Signal Processing. He has worked as a consultant for government and private institutes on statistical technologies for educational testing, biology, and mechanic engineering in South Korea. He is an associate editor of Intelligent Data Analysis (IDA) since 2003 and did the same job for Journal of the Korean Statistical Society (JKSS) and is on the reviewer panel of the American Mathematical Society. His present research interests include large-scale modeling for graphical models, structure learning, information optimization, and statistical analysis of neuro-activity data. His other activities include innovative education for pre-college Mathematics.

Speech Title: Information Theory and Sensor-target Geometry for Long-distance Target Localization
Abstract: We addressed a sensor arrangement problem for target localization based on the time difference of arrival (TDOA) data. It is assumed that the target is located far off from the sensors while the sensors are relatively close to each other. The estimators of the target location expressed in terms of the spherical coordinates are found to be uncorrelated when the sensors are arranged uniformly in angle in a concentric ring formation. We also found that the optimal arrangement of sensors is in the concentric ring formation and that the optimal arrangement changes in accordance with sensors' angular positions with respect to the line-of-sight vector from the reference sensor to the target when the arrangement is in a cross. Although the arrangement in an equilateral triangle is regarded as the best on average, the sensor arrangement in a cross is strongly recommended due to its high performance for a considerable range of the angular positions of the sensors with respect to the target.
Keywords: Information; maximum likelihood estimation; independence; optimization.