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Many location-based services target users in indoor environments. Similar to the case of dense urban areas where many obstacles exist, indoor localization techniques suffer from outlying measurements caused by severe multipath propaga??tion and non-line-of-sight (NLOS) reception. Obstructions in the signal path caused by static or mobile objects downgrade localization accuracy. We use robust multipath mitigation techniques to detect and filter out outlying measurements in indoor environments. We validate our approach using a power-based lo??calization system with GSM. We conducted experiments without any prior knowledge of the tracked device's radio settings or the indoor radio environment. We obtained localization errors in the range of 3m even if the sensors had NLOS links to the target device.

This study deals with indoor positioning using GSM radio, which has the distinct advantage of wide coverage over other wireless technologies. In particular, we focus on passive localization systems that are able to achieve high localization accuracy without any prior knowledge of the indoor environment or the tracking device radio settings. In order to overcome these challenges, newly proposed localization algorithms based on the exploitation of the received signal strength (RSS) are proposed. We explore the effects of non-line-of-sight communication links, opening and closing of doors, and human mobility on RSS measurements and localization accuracy. We have implemented the proposed algorithms on top of software defined radio systems and carried out detailed empirical indoor experiments. The performance results show that the proposed solutions are accurate with average localization errors between 2.4 and 3.2 meters.