Optimal and Unbiased Filtering With Colored Process Noise Using State Differencing

Yuriy S. Shmaliy, Shunyi Zhao, Choon Ki Ahn

Research output: Contribution to journalArticle

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 languageEnglish
Article number8638977
Pages (from-to)548-551
Number of pages4
JournalIEEE Signal Processing Letters
Volume26
Issue number4
DOIs
Publication statusPublished - 2019 Apr 1

Fingerprint

Nonsymmetric Algebraic Riccati Equation
Filtering
Riccati equations
Discrete-time Model
Augmentation
State-space Model
Impulse Response
Impulse response
Comparative Analysis
Markov processes
Gauss
Navigation
High Accuracy
Estimate
Simulation
Human

Keywords

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

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

Optimal and Unbiased Filtering With Colored Process Noise Using State Differencing. / Shmaliy, Yuriy S.; Zhao, Shunyi; Ahn, Choon Ki.

In: IEEE Signal Processing Letters, Vol. 26, No. 4, 8638977, 01.04.2019, p. 548-551.

Research output: Contribution to journalArticle

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