Proceedings ArticleDOI
A PNN- Jensen-Bregman Divergence symmetrization for a WLAN Indoor Positioning System
Osamah Ali Abdullah,Ikhlas Abdel-Qader +1 more
- pp 0362-0367
Reads0
Chats0
TLDR
A framework that incorporates the probabilistic neural network (PNN) and Jensen-Bregman Divergence (JBD) is proposed and the algorithm results have high accuracy with an error of less than 1m distance.Abstract:
Various techniques have been developed for Indoor Positioning Systems (IPS), a method that fingerprints the Received Signal Strength (RSS) of WiFi at specific places that can achieve high accuracy of about one meter at the exact location. A large range of indoor navigation needs and user services can be provided by using IPS, especially in unusual conditions such as being in large complex buildings or emergency healthcare needs, etc. In this paper, a framework that incorporates the probabilistic neural network (PNN) and Jensen-Bregman Divergence (JBD) is proposed. To validate our algorithm, the results were compared with PNN and kNN nearest neighbor. Where implemented inside an academic building, the algorithm results have high accuracy with an error of less than 1m distance.read more
Citations
More filters
Journal ArticleDOI
Extraction and Classification of Human Body Parameters for Gait Analysis
TL;DR: This work aims to develop an image processing system, with the use of the Microsoft Kinect sensor, which is capable to extract movement patterns for gait analysis and to present a comparative study of different pattern recognition methods for human identification.
Book ChapterDOI
Machine Learning Algorithm for Wireless Indoor Localization
TL;DR: A new framework based on symmetric Bregman divergence, which incorporates k-nearest neighbor (kNN) classification in signal space, was proposed, which was compared with the probabilistic neural network and multivariate Kullback-Leibler divergence.
Journal ArticleDOI
Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review
TL;DR: This analysis analyzes the use of Wi-Fi signals together with Machine Learning algorithms for indoor positioning using PRISMA guidelines to understand the current state of this field and to classify different parameters that are very useful for future studies.
Journal ArticleDOI
Convex Optimization via Symmetrical Hölder Divergence for a WLAN Indoor Positioning System
TL;DR: This paper proposes that symmetrical Hölder divergence, which is a statistical model of entropy that encapsulates both the skew Bhattacharyya divergence and Cauchy–Schwarz divergence that can be used to measure the statistical dissimilarities between the same exponential family for the signals that have multivariate distributions.
Journal ArticleDOI
Indoor WLAN localization via adaptive Lasso Bayesian inference and convex optimization
TL;DR: A framework consisting of k-mean-symmetrical-Hölder-divergence, a statistical model that encapsulates Cauchy-Schwarz divergence and skews Bhattacharyya divergence, to measure dissimilarities among signals that have multivariate distributions to optimize the accuracy of the indoor location estimation by solving the l1-minimization problem.
References
More filters
Proceedings ArticleDOI
RADAR: an in-building RF-based user location and tracking system
TL;DR: RADAR is presented, a radio-frequency (RF)-based system for locating and tracking users inside buildings that combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications.
Journal ArticleDOI
A Probabilistic Approach to WLAN User Location Estimation
TL;DR: The feasibility of this approach is demonstrated by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.
Journal ArticleDOI
Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing
TL;DR: Experimental results indicate that the proposed RSS-based indoor positioning system leads to substantial improvement on localization accuracy and complexity over the widely used traditional fingerprinting methods.
Journal ArticleDOI
Kernel-Based Positioning in Wireless Local Area Networks
TL;DR: It is shown that, due to the variability of RSS features over space, a spatially localized positioning method leads to improved positioning results and a kernelized distance calculation algorithm for comparing RSS observations to RSS training records is presented.
Journal ArticleDOI
WILL: Wireless Indoor Localization without Site Survey
TL;DR: This work designs WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones and shows that WILL achieves competitive performance comparing with traditional approaches.