Localization is a key component of any mobile robot application. For any task a mobile robot might need to perform, precise knowledge of its pose within its environment is critical. Mobile robots employ a multitude of sensors to estimate position, orientation, and mapping of its environment. Distance to wireless beacons through signal strength decay can be integrated into a simultaneous localization and mapping (SLAM) algorithm of a mobile robot equipped with a wireless transceiver, with an emphasis on indoor environments. However, radio signal strength does not predictably attenuate indoors as it does in open environments due to signal interference, absorption, and reflection from objects within the environment, inflicting unexpected amplification or decay at the receiver known as multipath interference. This causes erroneous distance estimations due to the unexpected changes in signal strength attenuation. In this research, models of radio propagation as it relates to the received signal strength indicator (RSSI) are explored along with localization techniques which utilize these models. For development and testing of RSSI-based localization techniques a simulation method has been described which utilizes a Markov chain to provide realistic multipath interference on simulated RSSI data. Using this simulation technique, a multipath filtration method is proposed and applied to a range-only SLAM algorithm.