Location Search Based on Subarea Fingerprinting and Surface Fitting for Indoor Positioning System

Abstract

The fingerprinting technique based on received signal strength (RSS) has been intensively researched for indoor localization in the last decade. Instead of using discrete reference points to build a fingerprint database, this paper applies the surface fitting technique to construct RSS spatial distribution functions and proposes two location search methods to find the target location. We also propose to use subarea division and determination to improve the fitting accuracy and search efficiency. In the offline phase, we divide the whole indoor environment into several subareas, construct a fingerprint for each subarea, and build a RSS fitting function for each access point in each subarea. In the online phase, we first determine to which subarea a target belongs, and then search its location according to the proposed exhaustive location search or gradient descent based search algorithm. We conduct both simulations and field experiments to verify the proposed scheme. The experiment results show that for the same reference point granularity, the proposed scheme can achieve on average 10% to 22% localization accuracy improvements, compared with the classical nearest neighbor-based fingerprinting method.