Indoor positioning WLAN based fingerprinting as supervised machine learning problem

Indoor positioning is one of the major topics in today's navigation and positioning research fields which is partially solved. The research community has not converged to a single, widely accepted solution that can achieve the desired accuracy at the required cost. Wireless Local Area Network (WLAN) based fingerprinting using Received Signal Strengths (RSS) is been considered as one solution for indoor positioning. This study structures this approach as supervised machine learning problem type where the target variable is the position and the features are the RSS values. There are compared the results obtained by from two analysis perspectives, regression and classification.