Wi-Fi RSS Based Indoor Positioning Using a Probabilistic Reduced Estimator

摘要

In this paper, we present an investigation of indoor objects positioning using the received Wi-Fi signal strength in the realistic environment with the presence of obstacles. Wi-Fi RSS based positioning is a promising alternative to other techniques for locating indoor objects. Two factors may lead to the low Wi-Fi RSS positioning accuracy: the existence of moving obstacles, and the limited number of available anchor nodes. We propose a novel approach to locating a target object in a given area by introducing a hidden factor for a reduced form of probabilistic estimator. This estimator is unbiased with the scalability in field size. With the selection of a Gaussian prior on this hidden factor characterizing the effects of RSS drop introduced by obstacles, we convert the positioning prediction into a maximum a posteriori problem, then apply expectation-maximization algorithm and conjugate gradient optimization to find the solution. Simulations in various settings show that the proposed approach presents better performance compared to other state-of-the-art RSS range-based positioning algorithms.