Modelling of Indoor Positioning Systems Based on Location Fingerprinting

In recent years, localization systems for indoor vicinity using the present wireless local area (WLAN) network infrastructure have been proposed. Such positioning systems create the usage of location fingerprinting instead of direction or time of arrival techniques for deciding the location of mobile users. However experimental study associated to such localization systems have been proposed, high attenuation and signal scattering related to greater density of wall attenuation still affecting the indoor positioning performance. This paper presents an analytical model for minimizing high signal attenuation effect for WLAN fingerprinting indoor positioning systems. The model employs the probabilistic algorithm that using signal relation method

Machine Learning Algorithm for Wireless Indoor Localization

Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data that are collected at distinct locations in indoor environments called reference points. In this study, a new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed. The coordinates of the target were determined as a weighted combination of the nearest fingerprints using Jensen-Bregman divergences, which unify the squared Euclidean and Mahalanobis distances with information-theoretic Jensen-Shannon divergence measures. To validate our work, the performance of the proposed algorithm was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence. The distance error for the developed algorithm was less than 1 m

An indoor positioning approach using sibling signal patterns in enterprise WiFi infrastructure

The indoor positioning technology plays an important role in the application scenarios requiring indoor location. In this paper, the WiFi signals under modern enterprise WiFi infrastructure and signal patterns between coexisting access points (APs) are investigated. Sibling signal patterns are defined and processed to generate Beacon APs that have higher confidence for positioning. Then a positioning approach using Beacon APs is proposed and shows improved positioning accuracy. The proposed schemes are fully designed, implemented and evaluated in a real-world environment, revealing its effectiveness and efficiency

An Implementation Approach and Performance Analysis of Image Sensor Based Multilateral Indoor Localization and Navigation System

Optical camera communication (OCC) exhibits considerable importance nowadays in various indoor camera based services such as smart home and robot-based automation. An android smart phone camera that is mounted on a mobile robot (MR) offers a uniform communication distance when the camera remains at the same level that can reduce the communication error rate. Indoor mobile robot navigation (MRN) is considered to be a promising OCC application in which the white light emitting diodes (LEDs) and an MR camera are used as transmitters and receiver respectively. Positioning is a key issue in MRN systems in terms of accuracy, data rate, and distance. We propose an indoor navigation and positioning combined algorithm and further evaluate its performance. An android application is developed to support data acquisition from multiple simultaneous transmitter links. Experimentally, we received data from four links which are required to ensure a higher positioning accuracy