NDLI: MAP RSS based joint localization and model identification under spatially autocorrelated noise

Content Provider	IEEE Xplore Digital Library
Author	Vallet Garcia, J.M.
Copyright Year	2015
Description	Author affiliation: Finnish Geospatial Res. Inst., Nat. Land Survey, Masala, Finland (Vallet Garcia, J.M.)
Abstract	This paper addresses the problem of model-based source localization using spatially autocorrelated received signal strength (RSS) measurements when the model parameters are not known a priori. This combined problem arises typically in situations in which a large number of observations are collected in positions close to each other in unknown environments. In our approach we model the RSS as a spatial Gaussian process characterized by an autocorrelation function. The position is then estimated by maximizing the log-likelihood after a previous analytical optimization over some of the model parameters. This technique allows the estimation of position without prior knowledge of these parameters. Although very convenient, we show experimentally that this method can be very sensitive to the geometry of the problem. As a solution we propose maximum a posteriori (MAP) estimation with indirect priors on the parameters affecting the mean of the predictions. Using data gathered from three different environments we demonstrate the effectiveness of the approach in real scenarios. On average, the localization errors achieved are 0.53, 0.99 and 0.86 m in a basketball field, a lobby and an office respectively.
Starting Page	1
Ending Page	8
File Size	1777499
Page Count	8
File Format	PDF
ISBN	9781479998586
ISSN	23250771
DOI	10.1109/ICL-GNSS.2015.7217135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-22
Publisher Place	Sweden
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wireless sensor networks Correlation Robot kinematics Estimation Mathematical model Optimization
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

An RSS-comparison based localization in wireless sensor networks

The analysis of localization algorithm of unscented particle filter based on RSS for linear wireless sensor networks

Detecting malicious nodes in RSS-based localization

Received signal strength-based sensor localization in spatially correlated shadowing

A comparative view on received signal strength (RSS) based location estimation in WSN

Distributed RSS-based localization in wireless sensor networks using convex relaxation

RSS-based localization for wireless sensor networks in practice

Gas source localization based on maximum likelihood with arbitrary deployment WSN

Data Accuracy Estimation for Cluster with Spatially Correlated Data in Wireless Sensor Networks

MAP RSS based joint localization and model identification under spatially autocorrelated noise

Similar Documents

An RSS-comparison based localization in wireless sensor networks

The analysis of localization algorithm of unscented particle filter based on RSS for linear wireless sensor networks

Detecting malicious nodes in RSS-based localization

Received signal strength-based sensor localization in spatially correlated shadowing

A comparative view on received signal strength (RSS) based location estimation in WSN

Distributed RSS-based localization in wireless sensor networks using convex relaxation

RSS-based localization for wireless sensor networks in practice

Gas source localization based on maximum likelihood with arbitrary deployment WSN

Data Accuracy Estimation for Cluster with Spatially Correlated Data in Wireless Sensor Networks

MAP RSS based joint localization and model identification under spatially autocorrelated noise