A novel real-time adaptive suboptimal recursive state estimation scheme for nonlinear discrete dynamic systems with non-Gaussian noise
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Kerim Demirbaş received the B.S. and M.S. degrees in electrical engineering from Istanbul Technical University (ITU), Turkey, in 1973; and the M.S., Engineer, and Ph.D. degrees, all in electrical engineering, from the University of California at Los Angeles (UCLA), USA, in 1977, 1979, and 1981 respectively. He was a senior research scientist at the Honeywell Systems and Research Center, Minneapolis, USA (1981–1984) and a faculty member of Electrical Engineering and Computer Science, the
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2019, Applied Ocean ResearchCitation Excerpt :Furthermore, different recursive algorithms, such as Extended Kalman Filter (EKF), were applied to the 3DBOT problem [7]. High sensitivity to initial conditions and measurement errors, particularly in the problem with poor observability, are the main shortcomings of this technique [8]. To enhance the performance of EKF, Unscented KF (UKF) was developed.
Online state estimation for discrete nonlinear dynamic systems with nonlinear noise and interference
2015, Journal of the Franklin InstituteCitation Excerpt :In this paper, an online recursive nonlinear state filtering and prediction scheme is proposed for nonlinear dynamic systems with nonlinear noise and interference representing a random signal such jamming or clutter. The proposed estimation scheme (PR) is an online estimation scheme which also prevents some state estimate divergences caused by the estimation approaches presented in [5–7] for nonlinear dynamic systems with nonlinear noise and interference, whereas the estimation schemes in [9,10] were developed for nonlinear discrete dynamic models without interference. The PR is based upon first approximating the disturbance noise, initial state, and interference with a discrete random disturbance noise, discrete initial state, and discrete interference; then quantizing the state, that is, representing the state model with a time varying state machine; and then an online suboptimal implementation of multiple composite hypothesis testing.
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2013, Digital Signal Processing: A Review JournalCitation Excerpt :This however comes with the expense of an applicability to low-dimensional problems only as the computational complexity growth exponentially with the dimension of the state space. To attenuate this computational burden, sophisticated techniques like adaptive discretization [12] or Rao–Blackwellization [13] have to be applied. In order to avoid the sample depletion problem of particle filters, the so-called Gaussian particle filter [14] has been proposed, which links sampling-based estimation with Gaussian filtering.
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Kerim Demirbaş received the B.S. and M.S. degrees in electrical engineering from Istanbul Technical University (ITU), Turkey, in 1973; and the M.S., Engineer, and Ph.D. degrees, all in electrical engineering, from the University of California at Los Angeles (UCLA), USA, in 1977, 1979, and 1981 respectively. He was a senior research scientist at the Honeywell Systems and Research Center, Minneapolis, USA (1981–1984) and a faculty member of Electrical Engineering and Computer Science, the University of Illinois at Chicago, USA (1984–1992). Since 1992, he has been with Middle East Technical University (METU), Turkey, where he is currently a professor of Electrical and Electronics Engineering. His current research interests are in the areas of communication systems, signal and image processing.