On the Use of Lookahead to Improve Wi-Fi Fingerprinting Indoor Localization Accuracy
Lu Shi, Nader Moayeri, Chang Li
Causality is a basic concept in system theory. In this paper we introduce the notions of causal and non-causal indoor localization. A non-causal localization system uses future signal and sensor measurements, in addition to past and present ones, to estimate the location of a person or an object at the present time. We provide example use cases where non-causal localization could prove useful. The main contribution of the paper is the development of an indoor localization system based on Wi-Fi fingerprinting and the Viterbi Algorithm that could be used in both causal and noncausal modes. Our proposed method finds the best "path" in a building matching a time series of Wi-Fi scan results made by the mobile device carried by the person to be located over a period of time. Our system improves localization accuracy by using the knowledge of building floor plans and the fact that humans do not move faster than a certain speed. We evaluate the performance of the causal and non-causal versions of our system and compare them against the basic WiFi fingerprinting localization system based on the KNN algorithm in an office building. Our empirical results show that both the causal and non-causal versions of our system outperform the basic fingerprinting system, with the non-causal version yielding significantly higher accuracy than the basic fingerprinting system.
Proceedings of 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
November 29-December 2, 2021
Lloret de Mar, ES
2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN)
, Moayeri, N.
and Li, C.
On the Use of Lookahead to Improve Wi-Fi Fingerprinting Indoor Localization Accuracy, Proceedings of 2021 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Lloret de Mar, ES, [online], https://doi.org/10.1109/IPIN51156.2021.9662582
(Accessed November 30, 2023)