RSS-based self-adaptive localization in dynamic environments

This paper focuses on optimal and automatic calibration of the propagation model of Received Signal Strength (RSS) based localization algorithms. Conventional RSS-based localization algorithms assume that optimal calibration is static and identical for all nodes, which limits its use to static environments. However realistic environments are dynamic, where each node should estimate its own optimal propagation model settings dependent on the node's hardware and location. We call this process Self-Adaptive Localization (SAL). SAL algorithms estimate the parameter settings from available localization measurements. We show that existing SAL algorithms significantly decrease the localization accuracy and stability. Our main contribution is that we determine the conditions under which SAL algorithms provide optimal results, that are shown to be constraints on the localization surface. Since the antenna orientation has a significant impact on RSS and thus optimal propagation model settings, we evaluated SAL in an environment with unknown and thus dynamic antenna orientations. Our measurements and simulations show that these constraints increase the accuracy by ~ 45% and the stability by ~ 70% in static and dynamic environments.