Extracting Stay Regions from UWB Indoor Trajectory Locationbased technology is key for ubiquitous society. To enhance the locationbased services, recognizing the places and the patterns where a person and an object have visited in addition to geographical locations is needed. Although some researchers have developed methods that extract outdoor stay regions from GPS trajectories to recognize the visiting places, there is no technology that recognizes stay regions, such as spatial location in a living house and an office building, from indoor trajectories. Technology for extracting indoor stay regions is required to achieve more intelligent indoorlocationbased services, such as smarthome and smartoffice. Therefore, we developed a method which extracts stay regions from an ultrawideband (UWB) indoor trajectory. An UWB indoor positioning technology provides location information with a fewtensofcentimeters error. Our developed method was evaluated comparing with conventional methods.

An improved pedestrian ttracking method based on wifi fingerprinting and pedestrian dead reckoning WiFi based positioning has great potential for use in indoor environments because WiFi signals are nearubiquitous in many indoor environments. With a Reference Fingerprint Map (RFM), fingerprint matching can be adopted for positioning. Much assisting information can be adopted for increasing the accuracy of WiFi based positioning. One of the most adopted pieces of assisting information is the Pedestrian Dead Reckoning (PDR) information derived from inertial measurements. This is widely adopted because the inertial measurements can be acquired through a Commercial Off The Shelf (COTS) smartphone. To integrate the information of WiFi fingerprinting and PDR information, many methods have adopted filters, such as Kalman filters and particle filters. A new methodology for integration of WiFi fingerprinting and PDR is proposed using graph optimization in this paper. For the WiFi based fingerprinting part, our method adopts the stateofart hierarchical structure and the Penalized Logarithmic Gaussian Distance (PLGD) metric. In the integration part, a simple extended Kalman filter (EKF) is first used for integration of WiFi fingerprinting and PDR results. Then, the tracking results are adopted as initial values for the optimization block, where WiFi fingerprinting and PDR results are adopted to form an concentrated cost function (CCF). The CCF can be minimized with the aim of finding the optimal poses of the user with better tracking results. With both realscenario experiments and simulations, we show that the proposed method performs better than classical Kalman filter based and particle filter based methods with both less average and maximum positioning error. Additionally, the proposed method is more robust to outliers in both WiFi based and PDR based results, which is commonly seen in practical situations.