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Michael Lunglmayr, Martin Krueger, Mario Huemer. Michael Lunglmayr ... Particle Filters popular in e.g. image recognition, positioning,... Aim of this work: ... – PowerPoint PPT presentation

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Title: Folie 1


1
A FEASIBILITY STUDY PARTICLE FILTERS FOR MOBILE
STATION RECEIVERS
CSNDSP 2006
Infineon
Michael Lunglmayr, Martin Krueger, Mario Huemer
2
Contents
  • Introduction
  • Simulation Model
  • Particle Filters
  • Particle Filters for Equalization
  • Simulation Results

3
Introduction
  • Particle Filters popular in e.g. image
    recognition, positioning,...
  • Aim of this work Equalization with particle
    filters
  • ?Symbol estimation for GSM/EDGE in a multipath
    propagation environment

4
Simulation Model
5
Simulation Model
6
Particle Filters
  • Connection to Equalization Estimate p(xkyk)
    and choose those state with the highest
    probability
  • Straight Forward Method calculate p(xkyk) for
    every state
  • ? Effort to high for practical systems

7
Particle Filters
  • Connection to Equalization Estimate p(xkyk)
    and choose those state with the highest
    probability
  • Straight Forward Method calculate p(xkyk) for
    every state
  • ? Effort to high for practical systems
  • Importance SamplingPrinciple If p(xkyk) would
    be known, it could be sampled? Particles
    then for N??

8
Particle Filters
  • Bad News p(xkyk) is not known because it is to
    be estimated!
  • But If we can sample a different probability
    functionq(xkxk-1,yk) (importance sampling
    function) and weight the particles with an
    importance weight

9
Particle Filters
  • Bad News p(xkyk) is not known because it is to
    be estimated!
  • But If we can sample a different probability
    functionq(xkxk-1,yk) (importance sampling
    function) and weight the particles with an
    importance weight
  • Example q(xkxk-1,yk) p(xkxk-1)?

10
PF for Equalization
  • Probability functions for GSM/EDGE

11
PF for Equalization
  • Probability functions for GSM/EDGE
  • Until now Sequential Importance Sampling
    (SIS)?But not very efficient yet!

12
Resampling
13
Particle Filter Algorithm
14
Implementation
15
Simulation Results
GMSK
16
Simulation Results
17
Conclusion
  • Particle Filters can outperform existing
    algorithms
  • Disadvantage computational complexity
  • But complexity depends only linearly on channel
    length? e.g. Promising use in extremely
    broadband communication systems with long
    impulse responses
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