Optimal and Unbiased Filtering With Colored Process Noise Using State Differencing

Yuriy S. Shmaliy; Shunyi Zhao; Choon Ki Ahn

doi:10.1109/LSP.2019.2898770

Optimal and Unbiased Filtering With Colored Process Noise Using State Differencing

Yuriy S. Shmaliy^*, Shunyi Zhao, Choon Ki Ahn

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

24 Citations (Scopus)

Abstract

This letter develops the Kalman and unbiased finite impulse response filtering algorithms for linear discrete-time state-space models with Gauss-Markov colored process noise (CPN) employing state differencing. The approach avoids problems caused by matrix augmentation, but requires solving a nonsymmetric algebraic Riccati equation to specify the system matrix modified for CPN. Higher accuracy of the algorithms proposed is demonstrated by simulation. A comparative analysis of filtering estimates is provided based on navigation data of walking humans.

Original language	English
Article number	8638977
Pages (from-to)	548-551
Number of pages	4
Journal	IEEE Signal Processing Letters
Volume	26
Issue number	4
DOIs	https://doi.org/10.1109/LSP.2019.2898770
Publication status	Published - 2019 Apr

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Kalman filter
State-space
colored process noise
state differencing
unbiased FIR filter

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/LSP.2019.2898770

Cite this

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abstract = "This letter develops the Kalman and unbiased finite impulse response filtering algorithms for linear discrete-time state-space models with Gauss-Markov colored process noise (CPN) employing state differencing. The approach avoids problems caused by matrix augmentation, but requires solving a nonsymmetric algebraic Riccati equation to specify the system matrix modified for CPN. Higher accuracy of the algorithms proposed is demonstrated by simulation. A comparative analysis of filtering estimates is provided based on navigation data of walking humans.",

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ER -

Optimal and Unbiased Filtering With Colored Process Noise Using State Differencing

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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