Spatio-temporal adaptive indoor positioning using an ensemble approach

We present a fingerprinting-based Wi-Fi indoor positioning method robust against temporal fluctuations and spatial instability in Wi-Fi signals. An ensemble is created using randomized weak position estimators, with the estimators specialized to different areas in the target environment and designed so that each area has estimators that rely on different subsets of stable APs. When conducting positioning, we cope with spatial instability by dynamically adjusting the weights of the weak estimators depending on the user's estimated location and cope with temporal fluctuations by dynamically adjusting the weights based on a periodic assessment of their performance using a particle filter tracker.