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Degerman, J., Wintenby, J. och Svensson, D. (2011) *Extended Target Tracking using Principal Components*.

** BibTeX **

@conference{

Degerman2011,

author={Degerman, Johan and Wintenby, Johannes and Svensson, Daniel},

title={Extended Target Tracking using Principal Components},

booktitle={Proceedings of the 14th International Conference on Information Fusion},

isbn={978-145770267-9},

pages={Art. no. 5977659},

abstract={The increased resolution in today’s radar systems
enables tracking of small targets. However, tracking both small
and large targets in a dense target scenario raises considerable
challenges. The data association of tracks to measurement groups
is highly dependent on good target extension models for filtering
and likelihood computation. In our attempt to design a tracker
for extended targets, we start by adopting the results from the
technique referred to as random matrices, which enables us to
separate the filtering into an extension and a kinematical part.
We re-define the measurement model and discard the assumption
of independent Gaussian-distributed plots. Instead we assume the
principal components to be Gaussian distributed. Then, through
a heuristic approach, we create a two-stage Kalman filter, where
the first stage estimates the principal components, and the second
stage estimates the centre of gravity, using the output from
the first stage as measurement uncertainty. The advantage of
having a Kalman filter with data-driven measurement noise over
a standard Kalman filter is demonstrated using simulated data,
where a significant improvement in terms of smaller errors and
reduced track loss is shown.},

year={2011},

keywords={Target tracking, extended targets, random matrices, principal components, Kalman filtering},

}

** RefWorks **

RT Conference Proceedings

SR Electronic

ID 140863

A1 Degerman, Johan

A1 Wintenby, Johannes

A1 Svensson, Daniel

T1 Extended Target Tracking using Principal Components

YR 2011

T2 Proceedings of the 14th International Conference on Information Fusion

SN 978-145770267-9

AB The increased resolution in today’s radar systems
enables tracking of small targets. However, tracking both small
and large targets in a dense target scenario raises considerable
challenges. The data association of tracks to measurement groups
is highly dependent on good target extension models for filtering
and likelihood computation. In our attempt to design a tracker
for extended targets, we start by adopting the results from the
technique referred to as random matrices, which enables us to
separate the filtering into an extension and a kinematical part.
We re-define the measurement model and discard the assumption
of independent Gaussian-distributed plots. Instead we assume the
principal components to be Gaussian distributed. Then, through
a heuristic approach, we create a two-stage Kalman filter, where
the first stage estimates the principal components, and the second
stage estimates the centre of gravity, using the output from
the first stage as measurement uncertainty. The advantage of
having a Kalman filter with data-driven measurement noise over
a standard Kalman filter is demonstrated using simulated data,
where a significant improvement in terms of smaller errors and
reduced track loss is shown.

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/local_140863.pdf

OL 30