Title :
Recursive Update Filtering for Nonlinear Estimation
Author_Institution :
Vehicle Dynamics & Controls Group, Charles Stark Draper Lab., Houston, TX, USA
fDate :
6/1/2012 12:00:00 AM
Abstract :
Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. This work proposes a novel nonlinear estimator whose additional computational cost is comparable to (N-1) EKF updates, where N is the number of recursions, a tuning parameter. The higher N the less the filter relies on the linearization assumption. A second algorithm is proposed with a differential update, which is equivalent to the recursive update as N tends to infinity.
Keywords :
Kalman filters; filtering theory; linearisation techniques; nonlinear estimation; nonlinear filters; extended Kalman filter; linear estimators; linearization assumption; nonlinear estimation; nonlinear filters; real-time navigation algorithms; recursive update filtering; tuning parameter; unscented Kalman filter; Estimation error; Jacobian matrices; Kalman filters; Measurement uncertainty; Noise; Random variables; Estimation; Kalman filtering; filtering; nonlinear estimation; recursive Kalman filter;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.2011.2178334