Reliable and accurate positioning systems are expected to significantly improve the safety for first responders and enhance their operational efficiency. To be effective, a first responder positioning systemmust provide room level accuracy during extended time periods of indoor operation. This thesis presents a system which combines a zero-velocity-update (ZUPT) aided inertial navigation system (INS), using a foot-mounted inertial measurement unit (IMU), with the use of opportunistic multi-frequency received signal strength (RSS) measurements. The system does not rely on maps or pre-collected data from surveys of the radio-frequency (RF environment; instead, it builds its own database of collected rss measurements during the course of the operation. New RSS measurements are continuously compared with the stored values in the database, and when the user returns to a previously visited area this can thus be detected. This enables loop-closures to be detected online, which can be used for error drift correction. The system utilises a distributed particle simultaneous localisation and mapping (DP-SLAM) algorithm which provides a flexible 2-D navigation platform that can be extended with more sensors. The experimental results presented in this thesis indicates that the developed rss slam algorithm can, in many cases, significantly improve the positioning performance of a foot-mounted INS.