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
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;
Conference_Titel :
Decision and Control, 1996., Proceedings of the 35th IEEE Conference on
Conference_Location :
Kobe
Print_ISBN :
0-7803-3590-2
DOI :
10.1109/CDC.1996.573485
