Re: [Geopriv] A new draft on Bayesian Location Identifiers

["Christian Hoene" <[e-mail]>]

Hi,

My recently submitted draft contains a few Latex equations. For those how are not used to read them, I wrote a small script to convert the draft to a pdf file that contains text without $.*$ and the rendered equations. 

With best regards,
 Christian



-----Original Message-----
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Sent: Thursday, October 14, 2010 10:59 AM
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Subject: [Geopriv] A new draft on Bayesian Location Identifiers

Hello,

we just have finished a first version of a draft, what presents a transmission format for uncertain, relative and transformed location estimates: http://www.ietf.org/id/draft-hoene-geopriv-bli-00.txt
Any comments and any feedback are welcomed.

With best regards,

 Christian Hoene

---------------------------------------------------------------
Dr.-Ing. Christian Hoene
Interactive Communication Systems (ICS), University of Tübingen Sand 13, 72076 Tübingen, Germany, Phone [phone] http://www.net.uni-tuebingen.de/

-----Original Message-----
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Subject: New Version Notification for draft-hoene-geopriv-bli-00 


A new version of I-D, draft-hoene-geopriv-bli-00.txt has been successfully submitted by Christian Hoene and posted to the IETF repository.

Filename:	 draft-hoene-geopriv-bli
Revision:	 00
Title:		 Bayesian Location Identifier
Creation_date:	 2010-10-14
WG ID:		 Independent Submission
Number_of_pages: 11

Abstract:
Location Generators cannot always provide exact measures of 
particular locations. Instead, they estimate the location of 
objects. More precisely, they use filters to aggregate noisy sensor 
data and to calculate probability density distributions of estimated 
positions. In location tracking applications, typically Kalman-type, 
Gaussian-Sum, and Particle Filters are used.

We believe that it is reasonable to use the outputs of those filters 
to describe a location estimate and its uncertainty, because they 
are the natural result of location tracking algorithms. In addition, 
the results of those filters can be feed into sensor fusion and 
decision making engines easily. 

Geometric representations such as polygons or ellipses might be 
demanded by an application. The output of filters can be converted 
to those application demanded shapes. However, these conversions 
come at the loss of precision and are not well understood 
scientifically. Thus, we think that transmitting filter results is a 
solution that is easier to implement.  

In this draft, we present a transmission format for PIDF-LO, which 
is based on the output of Kalman-type, Gaussian-Sum, and Particle 
Filters.
                                                                                  


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