Title :
Nonlinear Kalman filtering with semi-parametric Biscay distributions
Author_Institution :
Dept. of Eng. Sci., Oxford Univ., UK
fDate :
11/1/2001 12:00:00 AM
Abstract :
The problem of nonlinear estimation is reexamined, and a new semi-parametric representation of uncertainty called the Biscay distribution is presented. The Biscay distribution is combined with the extended Kalman filter (EKF) and a new filtering paradigm called the Biscay distribution filter (BDF) is developed. The BDF is provably optimal for linear estimation and generalizes naturally to nonlinear estimation. Further, the BDF is of the same computational order of complexity as the EKF. The BDF is compared with the EKF through an application in re-entry vehicle tracking
Keywords :
Kalman filters; filtering theory; nonlinear estimation; nonlinear filters; parameter estimation; sensor fusion; statistical analysis; tracking filters; Biscay distribution filter; approximate fusion method; computational complexity; filtering paradigm; linear estimation; multivariate inference; nonlinear Kalman filtering; nonlinear estimation; re-entry vehicle tracking; semi-parametric Biscay distributions; semi-parametric representation; uncertainty; Filtering; Kalman filters; Parameter estimation; State estimation; State-space methods; Statistical distributions; Target tracking; Uncertainty; Vehicles; Yield estimation;
Journal_Title :
Signal Processing, IEEE Transactions on
