Advances of the smooth variable structure filter: square-root and two-pass formulations

S. Andrew Gadsden; Andrew S. Lee

doi:10.1117/1.JRS.11.015018

9 March 2017 Advances of the smooth variable structure filter: square-root and two-pass formulations

S. Andrew Gadsden, Andrew S. Lee

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 015018 (March 2017). https://doi.org/10.1117/1.JRS.11.015018

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.

Citation Download Citation

S. Andrew Gadsden and Andrew S. Lee "Advances of the smooth variable structure filter: square-root and two-pass formulations," Journal of Applied Remote Sensing 11(1), 015018 (9 March 2017). https://doi.org/10.1117/1.JRS.11.015018

Received: 7 March 2016; Accepted: 13 February 2017; Published: 9 March 2017

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