An adaptive filtering approach to target tracking

Author

Madyastha, Venkatesh K. ; Calise, Anthony J.

Author_Institution

Sch. of Aerosp. Eng., Georgia Inst. of Technol., Atlanta, GA, USA

fYear

2005

fDate

8-10 June 2005

Firstpage

1269

Abstract

A method is presented for augmenting an extended Kalman filter with an adaptive element. The resulting estimator provides robustness to parameter uncertainty and unmodeled dynamics. The design of the adaptive element employs a linearly parameterized neural network. The network weights are adjusted on line using the filter error residuals. Boundedness of signals is proven using Lyapunov´s direct method and a backstepping argument. Simulations illustrate the theoretical results.

Keywords

Lyapunov methods; adaptive Kalman filters; neural nets; nonlinear filters; state estimation; target tracking; Lyapunov direct method; adaptive filtering; extended Kalman filter; filter error residuals; linearly parameterized neural network; parameter uncertainty; target tracking; unmodeled dynamics; Adaptive filters; Aerospace engineering; Neural networks; Parameter estimation; Robustness; State estimation; Stochastic systems; Target tracking; Uncertain systems; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2005. Proceedings of the 2005

ISSN

0743-1619

Print_ISBN

0-7803-9098-9

Electronic_ISBN

0743-1619

Type

conf

DOI

10.1109/ACC.2005.1470139

Filename

1470139

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3550731