Multivariable fuzzy inference with multi nearest neighbour for indoor WLAN localization based on RSS fingerprint

The power of fuzzy systems was approved in overcoming uncertainties and identifying ambiguities. Localization in wireless environments is found to have many uncertainties. In this paper a multivariable fuzzy inference system is combined with multi-nearest neighbor algorithm to estimate location of an object in an indoor wireless local area network. The proposed combination utilizes from the robustness of fuzzy systems to overcome some of the uncertainties associated with radio wave propagation and small scale signal variation. This work is focusing on the available localization techniques and how fuzzy systems are merged with other techniques to enhance the location estimation then it utilizes from the estimated position parameters and its correlation with the received signal strength, where the augmentation of one more input variable has grabbed non negligible attention in overcoming some of the uncertainties in location estimation especially the ones associated with the small scale variations due to changes in the physical environment.