Kalman filtering in non-Gaussian environment using efficient score function approximation

Wen-Rong Wu*, Amlan Kundu

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations

Abstract

The authors consider the problem of Kalman filtering in a non-Gaussian environment. It has been shown that a state estimate with a linear prediction corrected by a weighted score function can solve this problem, and the results are nearly optimal. However, the calculation of the score function requires a convolution of two density functions, which is difficult to implement except for simple cases. The authors propose an adaptive normal-expansion-based-distribution approximation for the efficient evaluation of the score function. It is shown that this method is simple and practically feasible. Simulations are also provided to demonstrate the success of the algorithm.

Original languageEnglish
Pages (from-to)413-416
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume1
DOIs
StatePublished - 8 May 1989
EventIEEE International Symposium on Circuits and Systems 1989, the 22nd ISCAS. Part 1 - Portland, OR, USA
Duration: 8 May 198911 May 1989

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