SLIDE 5 5 27.11.2017 Matthias Mögerle
[3] Andreas Haeberlen, Eliot Flannery, Andrew M. Ladd, Algis Rudys, Dan S. Wallach, and Lydia E. Kavraki. Practical robust localization over large-scale 802.11 wireless networks. Mobile computing and networking - MobiCom ’04 [4] Moustafa A Youssef, Ashok Agrawala, A Udaya Shankar, A Udaya Shankar, and A Udaya Shankar. WLAN location determination via clustering and probability distributions. Pervasive Computing and Communications, 2003.(PerCom 2003) [5] V. C. Ta, D. Vaufreydaz, T. K. Dao, and E. Castelli, “Smartphone-based user location tracking in indoor environment,” 2016 Int. Conf. Indoor Position. Indoor Navig. IPIN 2016, no. October, pp. 4–7, 2016. [6] Philipp Bolliger. Redpin - Adaptive, Zero-Con guration Indoor Localization through User Collaboration. Melt’08, pages 55–60, 2008. [7] Philipp Bolliger, Kurt Partridge, Maurice Chu, and Marc Langheinrich. Improving Location Fingerprinting through motion detection and asynchronous interval labeling. Lecture Notes in Computer, 5561 LNCS:37–51, 2009. [8] https://www.ekahau.com/
Research Goal and Related Work
The goal of this thesis is to enhance state of the art research on indoor positioning and combine it with an application for real world use. a) provide accurate indoor location in private smart environments without adding hardware b) focus on measurable results on usability, feasibility and accuracy
Research Goal
Related Work Sensors Clustering Labelling Comments
Gaussian Fit [3] Joint Clustering [4] IPIN Tracking Competition [5] RedPin [6] PILS [7] Ekahau Real-Time Location Systems [8] WiFi WiFi WiFi, GPS, IMU Sensors, Cameras WiFi WiFi, Accelerometer WiFi, RFID (active) Bayesian Inference & Signal Intensity Distr. Pre-processing k-Strongest Fingerprints Sensor fusion, Extreme gradient boost, dead reckoning Least square error to identify error between old and new locations Signal Intensity Distr. Probabilistic model na Initial Expert Labelling Topological Map Initial Expert Labelling Individual room-labels No floor plan required Floor Plan, Hierarchical Map No initial training Labelling by user during use Individual labels Individual labels No floor plan required Initial Expert Labelling Floor plan required Room-Level Accuracy Focus on performance gains Training on one explicit case Labelling by user Identifies user movements for feedback requests Industrialized solution RFID increase accuracy
