NDLI: A grid-scan maximum likelihood estimation with a bias function for indoor network localization

Content Provider	IEEE Xplore Digital Library
Author	Yuan Yang Yubin Zhao Kyas, M.
Copyright Year	2013
Description	Author affiliation: Dept. of Math. & Comput. Sci., Freie Univ. Berlin, Berlin, Germany (Yuan Yang; Yubin Zhao; Kyas, M.)
Abstract	Indoor range-based localization always suffers from imprecise range measurements, especially non-line-of-sight (N-LOS) errors. Probabilistic approaches like maximum likelihood estimation (ML) are efficient under measurement errors; however, to get the optimal solution poses a non-convex problem stagnating to local optima instead of the global optimum. More important, the convergence of the objective function can be greatly misled by the inappropriately modeled NLOS errors. We propose a grid-scan approach to approximate the global convergence of ML estimate and a bias function against the positive indoor ranging error and the NLOS effect, namely $GML^{b}.$ $GML^{b}$ combines the robustness of Bounding-box algorithm and the reality of an exponentially bias function to indoor ranging characteristics: the Bounding-box intersection ensures that the NLOS ranges cannot deviate the estimate far away; further, the bias function tunings the model to be specific for positive ranging errors. Monte Carlo simulations and real-world indoor experiments are implemented to investigate the efficiency of the strategies against the non-convex and NLOS problems. The Monte Carlo simulations demonstrate that GMLb achieves comparable accuracy to ML in an ideal case and higher robustness than ML series algorithms in the NLOS case. The experiment reveals that $GML^{b}$ is superior in scenarios rich of positive ranging errors, in particular, $GML^{b}$ performs the best in worst case. And $GML^{b}$ works well with a small number of grids, causing a low computational requirement on the ability-limited network devices.
Sponsorship	IEEE
Starting Page	1
Ending Page	9
File Size	2300998
Page Count	9
File Format	PDF
ISBN	9781479940431
DOI	10.1109/IPIN.2013.6817873
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-10-28
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Non-convex optimization Indoor network localization XML Probabilistic logic The NLOS error Distance measurement Robustness Positive ranging errors Maximum likelihood
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Correcting non-line-of-sight path length estimation for ultra-wideband indoor localization

Bias reduction based on maximum likelihood estimates with application in scan-based localization

A statistics-based least squares (SLS) method for non-line-of-sight error of indoor localization

A New Two-Step Ranging Algorithm in NLOS Environment for UWB Systems

Indoor user localization using particle filter and NLOS ranging model

A novel robust algorithm attenuating non-line-of-sight errors in indoor localization

Convex programming based robust localization in NLOS prone cluttered environments

Soft-decision based position estimation for wireless localization in indoor scenarios

Localization in WSN using maximum likelihood estimation with negative constraints based on particle swarm optimization

A grid-scan maximum likelihood estimation with a bias function for indoor network localization

Similar Documents

Correcting non-line-of-sight path length estimation for ultra-wideband indoor localization

Bias reduction based on maximum likelihood estimates with application in scan-based localization

A statistics-based least squares (SLS) method for non-line-of-sight error of indoor localization

A New Two-Step Ranging Algorithm in NLOS Environment for UWB Systems

Indoor user localization using particle filter and NLOS ranging model

A novel robust algorithm attenuating non-line-of-sight errors in indoor localization

Convex programming based robust localization in NLOS prone cluttered environments

Soft-decision based position estimation for wireless localization in indoor scenarios

Localization in WSN using maximum likelihood estimation with negative constraints based on particle swarm optimization

A grid-scan maximum likelihood estimation with a bias function for indoor network localization