I-D Action:draft-hoene-geopriv-bli-00.txt

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	Title           : Bayesian Location Identifier
	Author(s)       : C. Hoene, et al.
	Filename        : draft-hoene-geopriv-bli-00.txt
	Pages           : 11
	Date            : 2010-10-14

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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