Nonlinear regression Huber-based divided difference filtering. (April 2017)

• Record Type: Journal Article
• Authors: Li, Wei; Liu, Meihong
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 231:Number 5(2017:May)
• Page Start: 799
• Page End: 808
• Publication Date: 2017-04
• Subjects: Nonlinear regression -- Huber's technique -- nonlinear filtering -- robustness -- divided difference filter; Aeronautics -- Periodicals; Astronautics -- Periodicals; Airplanes -- Design and construction -- Periodicals; Aerospace industries -- Periodicals; 629.1
• DOI: 10.1177/0954410016642501
• Languages: English
• ISSNs: 0954-4100

Abstract

This article derives a nonlinear regression Huber-based divided difference filtering algorithm using a nonlinear regression approach for dynamic state estimation problems with non-Gaussian noises and outliers. In this approach, the nonlinear measurement model is directly used without linear or statistically linear approximation and the Huber-based divided difference filtering problem is solved using a Gauss–Newton approach. This new proposed filter method is then applied to a benchmark problem of estimating the trajectory of an entry body from discrete-time range data measured by a radar tracking station. Simulation results demonstrate the superior performance of the proposed filter as compared to the previous filter algorithms in the presence of non-Gaussian uncertainties.