Fuzzy Inference with Parameter Identification for Indoor WLAN Positioning

This paper considers the fuzzy inference as position estimator for WLAN indoor environments, based on received signal strength measurements RSS. The proposal algorithm includes a fuzzy inference which uses the k-nearest neighbor classification in signal space, where the position of target node is calculated as a weighted combination of nearest fingerprints, where the weights are estimated using enhanced Takagi–Sugeno fuzzy controller with multivariable inputs and parameter identification with constrained optimization. The new developed technique is proposed to enhance the accuracy of position estimation in WLAN indoor environments;