Fundamental Filtering Limitations in Linear Non-gaussian Systems
نویسندگان
چکیده
The Kalman filter is known to be the optimal linear filter for linear nonGaussian systems. However, nonlinear filters such as Kalman filter banks and more recent numerical methods such as the particle filter are sometimes superior in performance. Here a procedure to a priori decide how much can be gained using nonlinear filters, without having to resort to Monte Carlo simulations, is outlined. The procedure is derived in terms of the posterior Cramér-Rao lower bound. Results are shown for a class of standard distributions and models in practice. Copyright © 2005 IFAC
منابع مشابه
Fundamental Filtering Limitations in Linear Non-Gaussian Systems, Report no. LiTH-ISY-R-2681
The Kalman filter is known to be the optimal linear filter for linear non-Gaussian systems. However, nonlinear filters such as Kalman filter banks and more recent numerical methods such as the particle filter are sometimes superior in performance. Here a procedure to a priori decide how much can be gained using nonlinear filters, without having to resort to Monte Carlo simulations, is outlined....
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تاریخ انتشار 2004