The data rich nature of instrumented civil structures has brought attention to alternative applications outside of the traditional realm of structural health monitoring. An interest has been raised in using these vibration measurements for other applications such as human occupancy. An example of this is to use the vibrations measured from footsteps to locate occupants within a building. The localization of indoor footsteps can yield several benefits in areas such as security and threat detection, emergency response and evacuation, and building resource management, to name a few. The work described herein seeks to provide supplementary information to better define the problem of indoor footstep localization, and to investigate the use of several localization techniques in a real-world, operational building environment. The complexities of locating footsteps via indoor vibration measurements are discussed from a mechanics perspective using prior literature, and several techniques developed for localization in plate structures are considered for their applicability to indoor localization. A dispersion compensation tool is experimentally investigated for localization in an instrumented building. A machine learning approach is also explored using a nearest neighbor search. Additionally, a novel instrumentation method is designed based on a multi-point coupling approach that provides directional inference from a single point of measurement. This work contributes to solving the indoor footstep localization problem by consolidating the relevant mechanical knowledge and experimentally investigating several potential solutions.