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Journal of Applied Remote Sensing

Advances of the smooth variable structure filter: square-root and two-pass formulations
Author(s): S. Andrew Gadsden; Andrew S. Lee
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Paper Abstract

The smooth variable structure filter (SVSF) has seen significant development and research activity in recent years. It is based on sliding mode concepts, which utilize a switching gain that brings an inherent amount of stability to the estimation process. In an effort to improve upon the numerical stability of the SVSF, a square-root formulation is derived. The square-root SVSF is based on Potter’s algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation, and the results are compared with the popular Kalman filter. In addition, the SVSF is reformulated to present a two-pass smoother based on the SVSF gain. The proposed method is applied on an aerospace flight surface actuator, and the results are compared with the Kalman-based two-pass smoother.

Paper Details

Date Published: 9 March 2017
PDF: 19 pages
J. Appl. Remote Sens. 11(1) 015018 doi: 10.1117/1.JRS.11.015018
Published in: Journal of Applied Remote Sensing Volume 11, Issue 1
Show Author Affiliations
S. Andrew Gadsden, Univ. of Guelph (Canada)
Andrew S. Lee, Univ. of Guelph (Canada)
Univ. of Maryland, Baltimore County (United States)


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