Infrastructure-based Occupant Localization for Indoor Home Environments

People spend 62 percent of their time within the confines of their home. However, localization technologies such as GPS fail to accurately identify their indoor location. A key requirement of creating a smart home is both identifying each person and their current room location. Many techniques for identification or localization of people in indoor environments are being developed but most require either a privacy-invading camera system or participation by the tracked individuals to either perform actions or wear a device. These systems are useful in an office/industrial setting where the expectation of privacy is different, and an identification badge can be carried. In home environments, active participation and intrusive technologies are not ideal and should be avoided.
The goals of this dissertation are 1) to identify simultaneously and track multiple people within home environments in a non-intrusive manner and 2) to enable a new set of applications based on location and identity awareness. A custom hardware platform is developed and deployed at the top of doorways to collect both the height and direction of a person as he/she crosses through it. Next, signal processing algorithms are developed to convert the raw data into person-events. Finally, a tracking algorithm, based on simultaneously checking multiple paths, assigns identities and locations to the event stream. The results show the tracking system obtains a 90 percent room-level accuracy on average when tested on 3,000 manually recorded doorway crossings.