经典文章《approximate non-gaussian filtering with linear state and observatio

Juliet

【文件名】:06522@52RD_ng with linear state and observation relations.rar
【格　式】:rar
【大　小】:385K
【简　介】:Since R. Kalman and R. Bucy presented their famous papers on
filtering for discrete linear systems in 196Ck1961 [8],[9], a great number
of researchers have worked on the prediction, filtering, and smoothing
problem, extending Kalman-Bucy’s result, which holds for a linear state
and observation model and white noise sequences, to cases where the
state and observation relations are nonlinear or the noises correlated.
Comparatively little has been done, however, to extend the discrete
Kalman filter, which is optimal for Gaussian disturbances, to situations
where the distributions for the dsturbances are non-Gaussian. This may
partially be explained by the fact that the Kalman filter constitutes the
best linear filter, and by the argument that the disturbances often will
have approximately Gaussian distributions due to central limit theorem
type effects.
【目　录】:
I. INTRODUCTION
11. PRELIMINARIES
111. THE FIRST FILTER
IV. THE SECOND FILTER
v. SOME COMMENTS ON hVZWl4L IMPLE.iENTATI0
VI.SIMULATION RESULTS
VII. SLMMARY AND CONCLUDING COMMENTS