Lost in Space Where the Outer Bound of Localization Space Sets the Lower Bound on Localization Performance

This research reflects my theoretical and experimental journey into the lost space of wireless radio localization in the far field of 2.4GHz Commercial-OffThe-Shelf (COTS) radios. At the end of this journey, we arrive at the conclusion that existing phaseand time-based localization systems such as Radio Interferometric Positioning Systems (RIPS) and Time-Of-Flight (TOF) are not reliable in dynamic indoor environments. Our new localization system uses space-based rather than phaseor time-based measurements and shows adequate robustness for such environments. In the far field, the measured signals are a function of the four wave parameters time, position, temporal frequency and spatial frequency. These wave parameters are variables in propagation models that represent solutions to the Maxwell equations that govern the propagation of radio waves. Localization reduces to fitting themeasured signals to the appropriate propagationmodel at the unknown locations. We identify three types of localization systems based on how the measurements deal with wave parameters: RSS-, phaseand TOFbased systems. The first part of this research explores these individual systems. This journey starts by introducing a novel distributed connectivity-based localization system using a commonly employed flooding protocol. It exploits a certain part of the information in the protocol that other algorithms consider as redundant or false. This increases the localization performance in comparison with similar RSS-based systems, especially in harsh but static environments. In static environments, it is assumed that the optimal propagation model settings are known beforehand and are constant over space, time and hardware. In real indoor environments, these optimal propagation model settings depend on the locally and time varying permittivity and permeability of localization space. The challenge then becomes to determine the conditions under which RSS-based localization systems can calculate the optimal propagation model settings on-the-fly allowing for dynamic environments. These conditions turn out to be constraints on the localization surface acting as a spatial