A constrained extended Kalman filter for target tracking
Anders Erik Nordsjo - Saab Bofors Dynamics
Tue, 27 April 2004, 4:10 PM - 5:30 PM
An Extended Kalman Filter, EKF is proposed for tracking of the position and velocity of a moving target. The suggested method is based on a nonlinear model which, in addition, incorporates means for estimation of possible nonlinearities in the measurements of the target position.
In many practical scenarios, the initial estimates of target position and velocity will deviate significantly from the true ones. In order to reduce the impact of erroneous initial conditions and hence, obtain a faster initial convergence to an acceptable trajectory, a certain constrained form of the EKF, named the CEKF, is introduced. Although the original Kalman filter for a purely linear system is inherently stable, there is no guarantee that the linearized model used in the EKF gives a stable algorithm. Hence, it is interesting to note that the proposed CEKF under certain mild conditions renders an exponentially stable algorithm. It is shown that this latter method can conveniently be formulated as a nonlinear minimization problem with a quadratic inequality constraint.
Dr. Anders Erik Nordsjo - Saab Bofors Dynamics
Anders Erik Nordsjo received M.Sc. and Ph.D. degrees in electrical engineering from the Royal Institute of Technology, (KTH), Stockholm, Sweden.
Previously employed at ITT, Ericsson and KTH, he worked in the areas of data communication and mobile phone systems. He is currently with Saab Bofors Dynamics working with radar system analysis.
His research interests include nonlinear system identification, detection, estimation and tracking techniques, in particular with application to radar.