Localization in challenging environment using geometric and machine learning techniques
[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.
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