Title :
An extended unifying approach to multi-target tracking
Author :
Emre, E. ; Seo, J.
Author_Institution :
Dept. of Electr. Eng., Texas Tech. Univ., Lubbock, TX, USA
Abstract :
By means of global modeling of multitarget tracking (MTT), data-association and maneuver-estimation problems can be simultaneously solved using system identification techniques. With this approach, previously developed single-target tracking-acceleration estimation techniques can also be directly used for the MTT problem. In particular multiple-model (adaptive) Kalman filtering (MMKF) can be used to obtain the optimal solution. For computational considerations, one can apply some suboptimal solutions of MMKF such as one-step conditional maximum likelihood or maximum posteriori estimation
Keywords :
filtering and prediction theory; identification; radar theory; tracking; data-association; global modeling; maneuver-estimation; maximum posteriori estimation; multiple model Kalman filtering; multitarget tracking; system identification; Acceleration; Adaptive filters; Filtering; Kalman filters; Mathematical model; Maximum likelihood detection; Maximum likelihood estimation; Model driven engineering; System identification; Target tracking;
Conference_Titel :
Decision and Control, 1988., Proceedings of the 27th IEEE Conference on
Conference_Location :
Austin, TX
DOI :
10.1109/CDC.1988.194479
