Abstract
Recently, accurate location information of mobile stations (MSs) is desired for more and more applications. This thesis presents a proposal of a MS location tracking algorithm based on the maximum likelihood (ML) function in wireless cellular systems. The first part of this algorithm adopts measurements of uplink received signal strength (RSS) in a correlated shadowing environment to track a single path based on ML estimation. Aided with road information, the whole MS moving route, consisting of multiple moving paths, is then reconstructed by the mixed algorithm of the single path tracking combined with the detection of moving directions on road intersections by ML hypothesis testing. The observations from the simulation results show that, if the larger measuring data of tracking-step is adopted or more measurements from different BSs are gathered, then the better tracking performance will be achieved. Although the single path tracking may not exactly trace each moving segment of a target, with the help of road information the whole route can still be perfectly detected without any loss in the correlated shadowing microcellular environments.