Adaptive two-step filter with applications to bearings-only measurements problem

Zhou Di; Mu Chundi

Adaptive two-step filter with applications to bearings-only measurements problem

Zhou Di
, Mu Chundi

Tsinghua University

Research output: Contribution to conference › Paper › peer-review

Abstract

The two-step filter suits a class of nonlinear systems, which include a linear dynamic model and a nonlinear measurement model. This filter consists of a Kalman filter and a Gauss-Newton iterative algorithm. We integrate the two-step filter with a time-varying measurement noise statistical estimator to obtain an adaptive two-step filter, which still performs well in the case where the statistical properties of measurement noise are unknown apriori. The adaptive two-step filter is applied to the bearings-only measurement problem and the numerical results show that this filter is really effective.

Original language	English
Pages	1145-1150
Number of pages	6
State	Published - 1998
Externally published	Yes
Event	Guidance, Navigation, and Control Conference and Exhibit, 1998 - Boston, United States Duration: 10 Aug 1998 → 12 Aug 1998

Conference

Conference	Guidance, Navigation, and Control Conference and Exhibit, 1998
Country/Territory	United States
City	Boston
Period	10/08/98 → 12/08/98

Cite this

@conference{e1da2732e2b448879a3bff898f90bee7,

title = "Adaptive two-step filter with applications to bearings-only measurements problem",

abstract = "The two-step filter suits a class of nonlinear systems, which include a linear dynamic model and a nonlinear measurement model. This filter consists of a Kalman filter and a Gauss-Newton iterative algorithm. We integrate the two-step filter with a time-varying measurement noise statistical estimator to obtain an adaptive two-step filter, which still performs well in the case where the statistical properties of measurement noise are unknown apriori. The adaptive two-step filter is applied to the bearings-only measurement problem and the numerical results show that this filter is really effective.",

author = "Zhou Di and Mu Chundi",

note = "Publisher Copyright: {\textcopyright} 1998, American Institute of Aeronautics and Astronautics, Inc.; Guidance, Navigation, and Control Conference and Exhibit, 1998 ; Conference date: 10-08-1998 Through 12-08-1998",

year = "1998",

language = "英语",

pages = "1145--1150",

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TY - CONF

T1 - Adaptive two-step filter with applications to bearings-only measurements problem

AU - Di, Zhou

AU - Chundi, Mu

PY - 1998

Y1 - 1998

N2 - The two-step filter suits a class of nonlinear systems, which include a linear dynamic model and a nonlinear measurement model. This filter consists of a Kalman filter and a Gauss-Newton iterative algorithm. We integrate the two-step filter with a time-varying measurement noise statistical estimator to obtain an adaptive two-step filter, which still performs well in the case where the statistical properties of measurement noise are unknown apriori. The adaptive two-step filter is applied to the bearings-only measurement problem and the numerical results show that this filter is really effective.

AB - The two-step filter suits a class of nonlinear systems, which include a linear dynamic model and a nonlinear measurement model. This filter consists of a Kalman filter and a Gauss-Newton iterative algorithm. We integrate the two-step filter with a time-varying measurement noise statistical estimator to obtain an adaptive two-step filter, which still performs well in the case where the statistical properties of measurement noise are unknown apriori. The adaptive two-step filter is applied to the bearings-only measurement problem and the numerical results show that this filter is really effective.

UR - https://www.scopus.com/pages/publications/84959174747

M3 - 论文

AN - SCOPUS:84959174747

SP - 1145

EP - 1150

T2 - Guidance, Navigation, and Control Conference and Exhibit, 1998

Y2 - 10 August 1998 through 12 August 1998

ER -

Adaptive two-step filter with applications to bearings-only measurements problem

Abstract

Conference

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