A New Internet-Draft is available from the on-line Internet-Drafts directories.
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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