Sequential Monte Carlo method for the iFilter

Poisson point processes (PPP's) are very useful theoretical models for diverse applications. One of those is multi-target tracking of an unknown number of targets, leading to the intensity filter (iFilter) as a generalization of the probability hypothesis density (PHD) filter. This article deve...

Full description

Saved in:
Bibliographic Details
Published in14th International Conference on Information Fusion pp. 1 - 8
Main Authors Schikora, M., Koch, W., Streit, R. L., Cremers, D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2011
Subjects
Antenna arrays
Antenna measurements
Atmospheric measurements
Clutter
Intensity Filter
Monte Carlo methods
Multi-target tracking
Particle measurements
PHD Filter
Poisson point processes (PPP's)
Sequential Monte Carlo
Time measurement
Online AccessGet full text

Cover

More Information
Summary:Poisson point processes (PPP's) are very useful theoretical models for diverse applications. One of those is multi-target tracking of an unknown number of targets, leading to the intensity filter (iFilter) as a generalization of the probability hypothesis density (PHD) filter. This article develops a sequential Monte Carlo (SMC) implementation of the iFilter. In theory it was shown that the iFilter can estimate a clutter model from the measurements and thus does not need it as a-priori knowledge, like the PHD filter does. Our studies show that this property holds not only in simulations but also in real world applications. In addition it can be shown, that the performance of the PHD filter decreases substantially if the a-priori knowledge of the clutter intensity is chosen incorrectly.
ISBN:9781457702679
1457702673