A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things

Sajida Imran,Young-Bae Ko

doi:10.1155/2018/2976751

Abstract

WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.

Highlights

The outstanding advancement in IoT based applications has provoked the use of location-based systems (LBS) enabling mobile devices to provide a number of personal and commercial services including, but not limited to, object tracking [1], management and security, healthcare monitoring, personal navigation, and context awareness [2]
The proposed approach is compared with Principal Component Analysis (PCA) [12], Linear Discriminant Analysis (LDA) [11], and enhanced local Fisher discriminant analysis (ELFDA) [13]
The whole training set for radio map is divided into five parts: four parts are for tentatively building the model, while the remaining one is a validation set for evaluating the positioning performance

Summary

Introduction

The outstanding advancement in IoT based applications has provoked the use of location-based systems (LBS) enabling mobile devices to provide a number of personal and commercial services including, but not limited to, object tracking [1], management and security, healthcare monitoring, personal navigation, and context awareness [2]. Several candidate technologies are researched to solve indoor positioning problem including radio frequency (RF) [3], ultrawide band (UWB) [4], ultrasonic, and sound [5], visible light [6]. Most of these technologies provide comparatively accurate positioning. Dimensionality reduction techniques like FDA work well by restricting the RSS data to certain low dimensions. The indoor environment is complex and, due to effects like multipath propagation, the RSS data become nonlinear and multimodal. When the FDA is applied on a multimodal and nonlinear data, it will form several different clusters from a single multimodal sample. LFDA does not require multimodal samples to fall into a single cluster

Objectives

Results

Conclusion