Improved tracking of maneuvering targets: the use of turn-rate distributions for acceleration modeling

Tactically maneuvering targets are difficult to track since acceleration cannot be observed directly and the accelerations are induced by human control or an autonomous guidance system; therefore they are not subject to deterministic models. A common tracking system is the two-state Kalman Filter wi...

Full description

Saved in:
Bibliographic Details
Published inMFI'94 : 1994 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems :October 2-5, 1994, Las Vegas, Nevada, USA. pp. 515 - 520
Main Author Helferty, J.P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1994
Subjects
Acceleration
Coordinate measuring machines
Filters
Humans
Linear systems
Markov processes
Position measurement
Probability distribution
State estimation
Target tracking
Online AccessGet full text

Cover

More Information
Summary:Tactically maneuvering targets are difficult to track since acceleration cannot be observed directly and the accelerations are induced by human control or an autonomous guidance system; therefore they are not subject to deterministic models. A common tracking system is the two-state Kalman Filter with a Singer maneuver model where the second order statistics of acceleration is the same as a first order Markov process. The Singer model assumes a uniform probability distribution on the target's acceleration which is independent of the x and y direction. In practice, it is expected that targets have constant forward speed and an acceleration vector normal to the velocity vector, a condition not present in the Singer model. This paper extends the work of Singer by presenting a maneuver model which assumes constant forward speed and a probability distribution on the targets turn-rate. Details of the model are presented along with sample simulation results.< >
ISBN:9780780320727
0780320727
DOI:10.1109/MFI.1994.398410