Discrete Mobile Terminal Location Estimation Using Support Vector Machine

Mobile terminal location estimation has attrqcted much interest for ils applications in emergency calls, intelligent trffic control, intelligent transportation systems and resource allocation. In this pqper, a new implementation of data fusion technique for discrete mobile terminal location which is based on Support Vector Machine (SVM) is introduced. The rsw information, i.e., time of arrival TOA), angle of arrival (AOA) and received signal sftength (RSS) obtained fro* the survey points in a real dense urban environment, is post processed to create feature space. The feature space, which contains the information on the locations of the mobile users, is classified by a previously trained SVM network. The simulation results demonstrate that the location estimation ewor is less than I00m in almost 72% of the calls.

Discrete Mobile Terminal Location Estimation Using Support Vector Machine Keywords:

Mobile location , Data fusion , Pattern recognition , SVM