A Unified Estimator for Near-Field and Far-Field Localization Using TDOA
| 讲座名称 | A Unified Estimator for Near-Field and Far-Field Localization Using TDOA |
| 讲座时间 | 2020-01-06 09:30:00 |
| 讲座地点 | 北校区新科技楼1012会议室 |
| 讲座人 | Dominic K. C. Ho |
| 讲座人介绍 |  Dominic K. C. Ho is an IEEE Fellow and a Professor of the ECE at the University of Missouri, Columbia, MO, USA. He received the B.Sc. degree with First Class Honors, in 1988, and the Ph.D. degree in electronic engineering, in 1991, both from the Chinese University of Hong Kong. From September 1996 to August 1997, he was a Faculty in the Department of Electrical Engineering, University of Saskatchewan, Saskatoon, Canada. Since September 1997, he has been with the University of Missouri, USA. Dr. Ho’s research interests include sensor array processing, source localization, subsurface object detection, wireless communications, and adaptive processing. He was a Technical Chair of the IEEE ICASSP 2016. He has served as the Past Chair, the Chair, and the Vice-Chair of IEEE SAM Technical Committee. He was on the organizing committees of the IEEE SAM 2008 Workshop and the IEEE CAMSAP 2011. He was an Associate Editor of the IEEE TRANSACTIONS ON SIGNAL PROCESSING and the IEEE SIGNAL PROCESSING LETTERS. |
| 讲座内容 |
Localization of an object is fundamental in many engineering applications. Using of a number of sensors and observing the signal arrival time differences, we are able to determine the unique coordinates of an object if it is not distant. On the other hand, we can only obtain its DOA if it is far away. One has to know if the object is near or far to apply point or DOA estimation and such knowledge is often unavailable. Using a near-field model to locate a distant object will yield an unreasonable result caused by the thresholding effect and applying a far-field model to a near object will produce a significant DOA bias. This talk revisits the HBB Bound that illustrates the thresholding phenomenon in locating the point position of an object as its range to the sensor array increases. A unified TDOA positioning estimator based on an iterative implementation of the MLE is next introduced, which does not require the knowledge if the object is near or far. It automatically generates a point position if the object is near and yields the DOA if it is far. An initialization to the MLE based on a solution by the semidefinite relaxation is proposed. Simulation examples validate the good performance of the proposed estimator.
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