A radio frequency based indoor localization framework for supporting building emergency response operations

Building emergencies especially structure fires are big threats to the safety of building occupants and first responders. When emergencies occur, unfamiliar environments are difficult and dangerous for first responders to search and rescue, sometimes leading to secondary casualties. One way to reduce such hazards is to provide first responders with timely access to accurate location information. Despite its importance, access to the location information at emergency scenes is far from being automated and efficient. This thesis assesses the value of location information through a card game, and identifies a set of requirements for indoor localization through a survey. The most important five requirements are: accuracy, ease of on‐scene deployment, resistance to damages, computational speed, and device size and weight. The thesis introduces a radio frequency (RF) based indoor localization framework to satisfy these requirements. When no existing sensing infrastructure is accessible in a building and an ad‐hoc sensor network needs to be established, an environment aware beacon deployment (EASBL) algorithm is developed for supporting a sequence based localization schema. The algorithm is designed to achieve dual objectives of improving room‐level localization accuracy and reducing the effort required to deploy the ad‐hoc sensor network. When there is existing sensing infrastructure in the building, an iterative maximum likelihood estimation (IMLE) localization algorithm is developed for the framework. The algorithm integrates a maximum likelihood estimation technique for location computation. The algorithm also introduces an iterative process that mitigates impacts of radio signal’s multipath and fading effects on localization accuracy. Moreover, building information models are integrated to both algorithms. Building information plays an important role in mitigating multipath and fading effects in iterative location computation, enabling the metaheuristic based search for building‐specific satisfactory beacon deployment plans, and providing a graphical interface for user interaction and result visualization. The framework was validated in both simulation and field tests. The simulation involved two fire emergency scenarios in an office building, and reported room‐level accuracies of above 87.0% and coordinate-level accuracies of above 1.78 m for the EASBL, and room‐level accuracies of above 95.0% and coordinate-level accuracies of above 0.84 m for the IMLE. The field tests involved the same test bed and scenarios, and used a smartphone based prototype that implemented the framework. The field tests reported room‐level accuracies of above 82.8% and coordinate-level accuracies of above 2.29 m for the EASBL, and room‐level accuracies of above 84.6% and coordinate‐level accuracies of above 2.07 m for the IMLE. The framework also reduced the deployment effort of ad‐hoc sensor networks by 32.1%, was proven to be robust against partial loss of devices, and could promisingly satisfy other aforementioned requirements for indoor localization at building emergency scenes.