Radio map fusion for indoor positioning in wireless local area networks

This paper addresses the problem of indoor location estimation (LE) in wireless local area network (WLAN) using received signal strength (RSS). The difficultly of the problem lies in the complexity of the indoor propagation channel at operating WLAN frequency of 2.4 GHz, resulting in nonlinear and non-Gaussian spatio-temporal RSS properties. The first contribution of this paper is the introduction of a non-parametric Nadaraya-Watson estimator for LE using location fingerprints to capture the spatial distribution of RSS. Secondly, a novel method is proposed based on fusion of multiple location fingerprints at each survey location to cope with multimodal temporal probability distributions of RSS. Experimental results using real data collected in an office environment indicate that the proposed multiple-map method outperforms the KNN-based LE methods in terms of root mean square error.