Nonlinear Filters: Estimation and Applications: v. 400 (Lecture Notes in Economics and Mathematical Systems) - Tapa blanda

Sinopsis

For a non-linear filtering problem, the easiest approximation is to use the Taylor series expansion and apply the conventional linear recursive Kalman filter algorithm directly to the linearized non-linear measurement and transition equations. In this monograph, a non-linear and non-normal filter is proposed by utilizing Monte Carlo integration, in which a recursive algorithm of the weighting functions is derived. The density approximation approach gives an asymptotically unbiased estimator. Moreover, in terms of programming and computational time, the non-linear filter using Monte-Carlo integration can be easily extended to higher dimensional cases, compared with Kitagawa's non-linear filter using numerical integration.

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