Localization in challenging environment using geometric and machine learning techniques
Abstract
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] The establishment of robust sensor network applications requires accurate and non complex positioning techniques using a full or partial set of observations. In a practical environment, often not all the range measurements are good and some may be outliers. The presence of outliers can significantly reduce the performance of a localization algorithm. Detection and removal of the outliers are crucial to improve the positioning accuracy. On the other hand, joint source and sensor localization are essential in a wide range of problems involving array signal processing. The simultaneous source and sensor localization problems have received notable attention of researchers and are still open and challenging problems.
Degree
Ph. D.
Thesis Department
Rights
Access is limited to the campuses of the University of Missouri