DocumentCode
2112126
Title
A fast adaptive neural network scheme for multi-maneuvering target tracking
Author
Zhongliang, Jing ; Guowei, Zhang ; Hongren, Zhou
Author_Institution
Dept. of Autom. Control, Northwestern Polytech. Univ., Xian, China
fYear
1993
fDate
15-17 Dec 1993
Firstpage
3253
Abstract
In this paper, a new fast adaptive neural network scheme (FANNJPDAF) based on a joint probabilistic data association filter (JPDAF) for multi-maneuvering target tracking (MMTT) is presented. The computational burden of MMTT can be reduced drastically by a stochastic neural network. Computer simulations show that the scheme has high convergence performance, good accuracy and robustness to the uncertainty of target and clutter environments
Keywords
Hopfield neural nets; clutter; combinatorial mathematics; digital simulation; probability; simulated annealing; state estimation; accuracy; clutter; computational burden; computer simulations; fast adaptive neural network scheme; high convergence performance; joint probabilistic data association filter; multi-maneuvering target tracking; robustness; stochastic neural network; Adaptive filters; Adaptive systems; Computer networks; Computer simulation; Convergence; Neural networks; Robustness; Stochastic processes; Target tracking; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Conference_Location
San Antonio, TX
Print_ISBN
0-7803-1298-8
Type
conf
DOI
10.1109/CDC.1993.325806
Filename
325806
Link To Document