DocumentCode
321365
Title
A multiple model filter using different process noise levels
Author
Alouani, A.T. ; Rice, T.R.
Author_Institution
Tennessee Technol. Univ., Cookeville, TN, USA
Volume
2
fYear
1997
fDate
10-12 Dec 1997
Firstpage
1682
Abstract
This paper derives an optimal multiple model (MM) tracking filter using classical optimization theory. Two models are used: a constant velocity (CV) model with low state process noise, and a CV model but with large state process noise. One novel feature of this filter is that it does not require the a priori knowledge of the target transition probability matrix. Simulations are performed to show the online switching capability of the new filter as well as its performance
Keywords
kinematics; noise; optimisation; probability; state estimation; target tracking; tracking filters; constant velocity model; kinematic model; multiple model filter; optimization; probability matrix; state estimation; state process noise; target tracking; tracking filter; Acceleration; Detectors; Filtering theory; Filters; History; Kinematics; Noise level; Samarium; State estimation; Target tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location
San Diego, CA
ISSN
0191-2216
Print_ISBN
0-7803-4187-2
Type
conf
DOI
10.1109/CDC.1997.657791
Filename
657791
Link To Document