Hidden Naive Bayes Indoor Fingerprinting Localization Based on Best-Discriminating AP Selection

Abstract

Indoor fingerprinting localization approaches estimate the location of a mobile object by matching observations of received signal strengths (RSS) from access points (APs) with fingerprint records. In real WLAN environments, there are more and more APs available, with interference between them, which increases the localization difficulty and computational consumption. To cope with this, a novel AP selection method, LocalReliefF-C( a novel method based on ReliefF and correlation coefficient), is proposed. Firstly, on each reference location, the positioning capability of APs is ranked by calculating classification weights. Then, redundant APs are removed via computing the correlations between APs. Finally, the set of best-discriminating APs of each reference location is obtained, which will be used as the input features when the location is estimated. Furthermore, an effective clustering method is adopted to group locations into clusters according to the common subsets of the best-discriminating APs of these locations. In the online stage, firstly, the sequence of RSS observations is collected to calculate the set of the best-discriminating APs on the given location, which is subsequently used to compare with cluster keys in order to determine the target cluster. Then, hidden naive Bayes (HNB) is introduced to estimate the target location, which depicts the real WLAN environment more accurately and takes into account the mutual interaction of the APs. The experiments are conducted in the School of Environmental Science and Spatial Informatics at the China University of Mining and Technology. The results validate the effectiveness of the proposed methods on improving localization accuracy and reducing the computational consumption.

WLAN Fingerprint Indoor Positioning Strategy Based on Implicit Crowdsourcing and Semi-Supervised Learning

“…As a typical method of supervised learning, classification determines the category label of a new observation by learning from a set of samples with given category labels. In our previous study, we addressed indoor positioning as a classification problem [28]. The received signal strength (RSS) vectors are treated as samples, and the locations are treated as category labels.…”

CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles

“…Song et al [13] analyzed FP collection as an AP relevancy problem. Hidden Naïve Bayes (HNB) was used as a mechanism to infer the most relevant APs and suggested that redundant APs may be obviated for each RP through a variant of ReliefF with the Pearson product-moment correlation coefficient (PPMCC).…”

“…Utilizing these to capture RSSI fingerprint (FP) is conveniently possible with device as simple as smartphones or phablets. Wi-Fi fingerprint-based localization has the following benefits: no requirement of extra hardware at both sender and receiver sides; utilization of already existent infrastructure; easily implementable; and no essential need of propagation model building which may or may not depict real signal propagation at run time [13].…”

Current Trends and Challenges in Location-Based Services

“…As an example, WiFi fingerprinting attracted significant research interest in the past few years. Song et al [6], for instance, focused on this aspect, and proposed a WiFi fingerprinting method based on hidden naïve Bayes to provide indoor positioning. To improve location accuracy and computational consumption, a method based on ReliefF and the correlation coefficient was proposed to select the best discriminating access points (APs) in WiFi fingerprinting.…”