DocumentCode
306945
Title
Track-independent estimation schemes for registration in a network of sensors
Author
Abbas, H. ; Xue, D.P. ; Farooq, M. ; Parkinson, G. ; Blanchette, M.
Author_Institution
Dept. of Electr. & Comput. Eng., R. Mil. Coll. of Canada, Kingston, Ont., Canada
Volume
3
fYear
1996
fDate
11-13 Dec 1996
Firstpage
2563
Abstract
Several methods for estimating registration biases in a network of sensors are presented in this paper. These methods are track-independent meaning that they do not require assumptions on target dynamics models. Based on the simulation studies, the applicability, accuracy and efficiency of these methods are discussed and compared with the track-dependent Kalman filtering method. Recommendations are made on the choice of the methods
Keywords
Kalman filters; filtering theory; maximum likelihood estimation; neural nets; sensor fusion; target tracking; tracking; accuracy; applicability; efficiency; registration biases; sensors network; track-dependent Kalman filtering method; track-independent estimation schemes; Azimuth; Coordinate measuring machines; Filtering; Intelligent networks; Kalman filters; Position measurement; Q measurement; Radar tracking; Sensor fusion; Target tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location
Kobe
ISSN
0191-2216
Print_ISBN
0-7803-3590-2
Type
conf
DOI
10.1109/CDC.1996.573485
Filename
573485
Link To Document