| Paper: | SPTM-L2.4 | ||
| Session: | Particle Filtering and Other Tracking Algorithms | ||
| Time: | Tuesday, May 16, 15:00 - 15:20 | ||
| Presentation: | Lecture | ||
| Topic: | Signal Processing Theory and Methods: Adaptive Systems and Filtering | ||
| Title: | Reduced sigma point filtering for partially linear models | ||
| Authors: | Mark Morelande, University of Melbourne, Australia; Branko Ristic, Defence Science and Technology Organisation (DSTO), Australia | ||
| Abstract: | A method for performing unscented Kalman filtering with a reduced number of sigma points is proposed. The procedure is applicable when either the process or measurement equations are partially linear in the sense that only a subset of the elements of the state vector undergo a nonlinear transformation. It is shown that for such models second-order accuracy in the moments required for the unscented Kalman filter recursion can be obtained using a number of sigma points determined by the number of nonlinearly transformed elements rather than the dimension of the state vector. A procedure for computing the sigma points is developed. An application of the proposed method to smoothed target state estimation from bearings measurements is presented. | ||
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