Indoor location using received signal strength of IEEE 802.11b access point

In this paper, the fingerprinting technique is employed to locate a mobile user inside a building. The fingerprint information, collected from real in-building measurements, is formed by three IEEE 802.11b access points´ signal strength data received by the mobile user. Three different pattern-matching algorithms have been studied: the multi-layer perceptron (MLP) neural network, the generalized radial neural network (GRNN) and the K-nearest neighbours (KNN) algorithm. Their performances in terms of localization accuracy are compared on both training and testing data. Results show that the K-nearest neighbours gives the best localization accuracy. The effect of the measurement´s grid spacing has also been investigated. Experimental results show that the localization accuracy increases when the grid spacing decreases. However, when the spacing reaches a certain threshold value, the accuracy starts to deteriorate. It can be shown that, in reality, the localization accuracy is improved even after the considered threshold value