Indoor pervasive applications depend on reliable indoor localization solutions. Indoor localization using WiFi is gaining ubiquitous usage owing to its simplicity and inexpensiveness. A conventional method of localization is trilateration, which can be accomplished using signal strength or time of flight of a radio signal between receiver and transmitter. However, trilateration is prone to errors in accuracy that can occur due to various factors. A common reason for the failure of trilateration is due to the errors in distance estimation resulting in a poor quality of trilateration. In this paper, we propose a novel weighted adaptive location estimation (WALE) algorithm. The proposed WALE algorithm provides an accurate localization in comparison to trilateration by taking into account the quality and properties of the circle overlaps. Based on the overlap properties a distance re-estimation and classification of points based on whether they are trilaterable is performed. A maximum likelihood estimation over a weighted grid of this region provides the location estimate. Our experiments over real indoor test-beds have demonstrated that our algorithm provides high accuracies with high stability in comparison to other algorithms. We achieve high accuracies ranging from 1.20 to 2.20 m in majority of the cases and an average of 2.98 m in a large office space with a standard deviation of 1.65.
D. Sundar, Sendil, S., Subramanian, V., and Dr. Vidhya Balasubramanian, “WALE: a Weighted Adaptive Location Estimation Algorithm”, Journal of Ambient Intelligence and Humanized Computing, 2019.