Indoor Localization Based on Curve Fitting and Location Search Using Received Signal Strength

Indoor localization based on received signal strength (RSS) has attracted considerable attention in both academia and industry due to the wide deployment of wireless local area networks. In this paper, we propose a novel indoor localization scheme based on curve fitting (CF) and location search. In the offline phase, we divide the whole environment into some subareas and create a fingerprint for each subarea. We then apply the CF technique to construct a fitted RSS-distance function for each transmitter in each subarea. The online positioning phase consists of two steps. In the first step, we determine a subarea to which a mobile device belongs. In the second step, we propose two location search algorithms, namely exhaustive search and gradient descent search, to find a location within the selected subarea such that the sum of distance errors can be minimized. We conduct field experiments to examine the proposed algorithms. The results show that our algorithms can obtain approximately 20% improvement in localization accuracy compared with the classical fingerprinting-based and lateration-based localization algorithms.