FUNDAMENTAL FILTERING LIMITATIONS IN LINEAR NON-GAUSSIAN SYSTEMS

Fundamental Filtering Limitations in Linear Non-gaussian Systems

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. ...

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....

Polynomial Filtering for Linear Discrete Time Non-gaussian Systems

In this work we propose a new filtering approach for linear discrete time non-Gaussian systems that generalizes a previous result concerning quadratic filtering [A. De Santis, A. Germani, and M. Raimondi, IEEE Trans. Automat. Control, 40 (1995) pp. 1274–1278]. A recursive νth-order polynomial estimate of finite memory 1 is achieved by defining a suitable extended state which allows one to solve...

Fundamental Limitations in Filtering and Control

Design Limitations in Linear Filtering

This paper examines the fundamental design trade-oos which apply to all linear ltering problems. These trade-oos express the inescapable sensitivity of all linear lters to disturbances of various sources. The constraints allow one to determine, a priori, whether or not a desired level of lter performance is attainable and how diierent arrangements of the measurement system are related to achiev...