Term of Award
Master of Science, Computer Science (M.S.C.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Computer Science
Committee Member 1
Committee Member 2
Accurate indoor localization often depends on infrastructure support for distance estimation in range-based techniques. One can also trade off accuracy to reduce infrastructure investment by using relative positions of other nodes, as in range-free localization. Even for range-based methods where accurate Ultra-WideBand (UWB) signals are used, non line-of-sight (NLOS) conditions pose significant difficulty in accurate indoor localization. Existing solutions rely on additional measurements from sensors and typically correct the noise using a Kalman filter (KF). Solutions can also be customized to specific environments through extensive profiling. In this work, a range-based indoor localization algorithm called PSO - Kalman Filter Fusion (PKFF) is proposed that minimizes the effects of NLOS on localization error without using additional sensors or profiling. Location estimates from a windowed Particle Swarm Optimization (PSO) and a dynamically adjusted KF are fused based on a weighted variance factor. PKFF achieved a 40% lower 90-percentile root-mean-square localization error (RMSE) over the standard least squares trilateration algorithm at 61 cm compared to 102 cm.
Bupe, Paul Jr, "Accurate Range-based Indoor Localization Using PSO-Kalman Filter Fusion" (2020). Electronic Theses and Dissertations. 2048.
Research Data and Supplementary Material