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
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;
Conference_Titel :
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-1298-8
DOI :
10.1109/CDC.1993.325806
