This project aims to develop the robust geolocation algorithms that can mitigate errors in measurements of the required measures and satellite locations to enhance the localization accuracy. Robust optimization techniques will be employed where the RFI location is obtained by solving the underlying estimation problems (e.g., maximum likelihood, least square estimators) considering the potential uncertainty regions. In particular, robust optimization using average and worst-case solution approaches will be taken. In addition, this project will consider exploiting the statistics of the different error sources to improve the localization accuracy. Performance evaluation of the proposed geolocation schemes using both mathematical modeling and computer simulation approaches will be conducted. Specifically, comparison of the estimation performance of developed geolocation algorithms with the corresponding Cramér Rao lower bound will be performed to reveal their achievable performance with respect to the best performance limit. Hybrid geolocation techniques exploiting the advantages of different designed schemes will be devised to further improve the estimation performance.
