Indoor Location Fingerprinting Based on Data Reduction

Abstract

Agent localization in indoor wireless environments is a challenging issue. Numerous techniques have been developed. Location fingerprinting, which is based on received signal strength measurements, is a frequently used approach for indoor applications. In this paper, we examine the possibility to obtain the location fingerprinting method characterized with more accurate mapping between the signal-space and the physical-space. An implemented well-known weighted k-nearest neighbor (WkNN) method is enhanced by two steps: a) pre-processing by the unsupervised learning technique during radio map building and b) post-processing of initial estimates obtained by the WkNN localization method. In this post-processing step signal-space and physical-space are transformed and mapped using two techniques of the dimension reduction: principal component analysis and multidimensional scaling. The aim of this transformation step is to de-correlate and refine initially obtained location estimates. Parameters such as number of access points and number of nearest reference nodes are examined for their impact on accuracy of the presented localization techniques. Performances are examined and verified through the experiments with real environment data.