Measurement preprocessing-based variable dimension PDAF for maneuvering target tracking in clutter

This paper presents a measurement preprocessing based variable dimension probabilistic data association filter (PDAF) for tracking a single maneuvering target in clutter. The measurement preprocessing scheme is derived from a maximum likelihood method and utilizes validated measurements for yielding...

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Bibliographic Details
Published in2011 6th IEEE Conference on Industrial Electronics and Applications pp. 996 - 1001
Main Authors Ho, T.-J, Chen, Y.-J
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Acceleration
Clutter
Computational modeling
Covariance matrix
Kalman filter
measurement preprocessing
Noise
probabilistic data association filter
Target tracking
Time measurement
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ISBN9781424487547
1424487544
ISSN2156-2318
DOI10.1109/ICIEA.2011.5975732

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Summary:This paper presents a measurement preprocessing based variable dimension probabilistic data association filter (PDAF) for tracking a single maneuvering target in clutter. The measurement preprocessing scheme is derived from a maximum likelihood method and utilizes validated measurements for yielding target's estimated accelerations and velocities which lead to preprocessed measurements. The proposed preprocessing method is integrated into a variable dimension PDAF which uses preprocessed measurements as its inputs. Simulation results show that the proposed tracking algorithm can achieve improved performance.
ISBN:9781424487547
1424487544
ISSN:2156-2318
DOI:10.1109/ICIEA.2011.5975732