Title :
Optimum nonlinear filtering
Author :
Haykin, Simon ; Yee, Paul ; Derbez, Eric
Author_Institution :
Commun. Res. Lab., McMaster Univ., Hamilton, Ont., Canada
fDate :
11/1/1997 12:00:00 AM
Abstract :
This paper is composed of two parts. The first part surveys the literature regarding optimum nonlinear filtering from the (continuous-time) stochastic analysis point of view, and the other part explores the impact of recent applications of neural networks (in a discrete-time context) to nonlinear filtering. In particular, the results obtained by using a regularized form of radial basis function (RBF) networks are presented in fair detail
Keywords :
continuous time filters; discrete time filters; feedforward neural nets; nonlinear filters; optimisation; signal processing; stochastic processes; continuous-time analysis; discrete-time analysis; neural networks; nonlinear filtering; optimum nonlinear filtering; radial basis function networks; signal processing; stochastic analysis; Calculus; Differential equations; Filtering; Kalman filters; Neural networks; Nonlinear filters; Radar tracking; State estimation; Stochastic processes; Target tracking;
Journal_Title :
Signal Processing, IEEE Transactions on
