Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Particle Methods for Indoor Tracking in WiFi Networks

von Ehrenheim, Vilhelm (2013) FMS820 20131
Mathematical Statistics
Abstract (Swedish)
This thesis treats the problem of positioning in WiFi networks and proposes a
solution using hidden Markov models and particle lters based on sequential importance
sampling with resampling. Hidden Markov models prove to be a powerful
framework for this type of problem exhibiting both an intuitive and adaptive model
structure. One of the diculties encountered when ltering this model is that it
is increasing in dimension over time. To get around this problem in this thesis, a
method called sequential importance sampling is used which makes it possible to
update the estimation sequentially using the previous estimate to calculate the new
one. In addition, parameter inference is conducted using expectation-maximizationbased
likelihood... (More)
This thesis treats the problem of positioning in WiFi networks and proposes a
solution using hidden Markov models and particle lters based on sequential importance
sampling with resampling. Hidden Markov models prove to be a powerful
framework for this type of problem exhibiting both an intuitive and adaptive model
structure. One of the diculties encountered when ltering this model is that it
is increasing in dimension over time. To get around this problem in this thesis, a
method called sequential importance sampling is used which makes it possible to
update the estimation sequentially using the previous estimate to calculate the new
one. In addition, parameter inference is conducted using expectation-maximizationbased
likelihood inference in the proposed model. To estimate model parameters
in a hidden Markov model smoothing is done to reconstruct the underlying state
sequence given all data observations. These states are then used to calculate suf-
cient statistics for the sought parameters. Finding the smoothing distribution it
might be necessary to use a large number of particles in the lter to overcome degeneration
of the approximation. A method called xed-lag smoothing is therefore
investigated in order to get better estimates. Finally we illustrate the model and
the estimation algorithms on data from a real world application. (Less)
Please use this url to cite or link to this publication:
author
von Ehrenheim, Vilhelm
supervisor
organization
course
FMS820 20131
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
3813595
date added to LUP
2013-06-14 16:19:46
date last changed
2013-06-14 16:19:46
@misc{3813595,
  abstract     = {This thesis treats the problem of positioning in WiFi networks and proposes a
solution using hidden Markov models and particle lters based on sequential importance
sampling with resampling. Hidden Markov models prove to be a powerful
framework for this type of problem exhibiting both an intuitive and adaptive model
structure. One of the diculties encountered when ltering this model is that it
is increasing in dimension over time. To get around this problem in this thesis, a
method called sequential importance sampling is used which makes it possible to
update the estimation sequentially using the previous estimate to calculate the new
one. In addition, parameter inference is conducted using expectation-maximizationbased
likelihood inference in the proposed model. To estimate model parameters
in a hidden Markov model smoothing is done to reconstruct the underlying state
sequence given all data observations. These states are then used to calculate suf-
cient statistics for the sought parameters. Finding the smoothing distribution it
might be necessary to use a large number of particles in the lter to overcome degeneration
of the approximation. A method called xed-lag smoothing is therefore
investigated in order to get better estimates. Finally we illustrate the model and
the estimation algorithms on data from a real world application.},
  author       = {von Ehrenheim, Vilhelm},
  language     = {eng},
  note         = {Student Paper},
  title        = {Particle Methods for Indoor Tracking in WiFi Networks},
  year         = {2013},
}