Compressed Multivariate Kernel Density Estimation for WiFi Fingerprint-based Localization

Abstract

WiFi fingerprint-based localization is one of the most attractive and promising techniques targeted for indoor localization, and has attained much attention in the past decades. In addition to improving localization accuracy, various efforts have been devoted to efficiently building a radio map which is normally tedious and laborious. Therefore, this paper proposes an efficient approach for building compact radio maps based on compressed multivariate kernel density estimation (CMKDE), in the sense that only a few received signal strength (RSS) measurements are required and the resulting radio maps are far less than the sizes of traditional radio maps. Extensive experiments are carried out in a real scenario of nearly 1000 m2 during several working days, and a comparison is made with two existing popular solutions including the Gaussian process regression (GPR) and another approach based on kernel density. It is shown that the proposed method outperforms its counterparts in terms of both robustness and accuracy.