A simulation of SINS/GPS integrated system using H∞ filtering

Hui Li; Xiuyun Meng; Zaozhen Liu

A simulation of SINS/GPS integrated system using H_∞ filtering

Hui Li^*, Xiuyun Meng, Zaozhen Liu

^*Corresponding author for this work

Beijing Institute of Technology

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

Abstract

Kalman filtering provides an optimal estimate in terms of minimum mean square error for a SINS/GPS integrated system, H_∞ filtering provides an estimate to solve uncertain problem while there exists uncertainty in system. In this paper, a modified H_∞ filter is discussed when the intensity of noise is approximately grasped and a loosely coupled system of SINS/GPS integration is introduced in detail. A loosely coupled system using the modified H_∞filter is simulated. Simulation shows the modified H_∞ filtering can almost achieve the same accuracy as that of Kalman filtering when noise characteristics is well known, and overcome the conservatism of H_∞ filtering.

Original language	English
Pages	1294-1298
Number of pages	5
Publication status	Published - 2005
Event	Asian Simulation Conference 2005, ASC 2005 and the 6th International Conference on System Simulation and Scientific Computing, ICSC 2005 - Beijing, China Duration: 24 Oct 2005 → 27 Oct 2005

Conference

Conference	Asian Simulation Conference 2005, ASC 2005 and the 6th International Conference on System Simulation and Scientific Computing, ICSC 2005
Country/Territory	China
City	Beijing
Period	24/10/05 → 27/10/05

Keywords

GPS
Kalman Filter
SINS

Cite this

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title = "A simulation of SINS/GPS integrated system using H∞ filtering",

abstract = "Kalman filtering provides an optimal estimate in terms of minimum mean square error for a SINS/GPS integrated system, H∞ filtering provides an estimate to solve uncertain problem while there exists uncertainty in system. In this paper, a modified H∞ filter is discussed when the intensity of noise is approximately grasped and a loosely coupled system of SINS/GPS integration is introduced in detail. A loosely coupled system using the modified H∞filter is simulated. Simulation shows the modified H∞ filtering can almost achieve the same accuracy as that of Kalman filtering when noise characteristics is well known, and overcome the conservatism of H∞ filtering.",

keywords = "GPS, Kalman Filter, SINS",

author = "Hui Li and Xiuyun Meng and Zaozhen Liu",

year = "2005",

language = "English",

pages = "1294--1298",

note = "Asian Simulation Conference 2005, ASC 2005 and the 6th International Conference on System Simulation and Scientific Computing, ICSC 2005 ; Conference date: 24-10-2005 Through 27-10-2005",

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TY - CONF

T1 - A simulation of SINS/GPS integrated system using H∞ filtering

AU - Li, Hui

AU - Meng, Xiuyun

AU - Liu, Zaozhen

PY - 2005

Y1 - 2005

N2 - Kalman filtering provides an optimal estimate in terms of minimum mean square error for a SINS/GPS integrated system, H∞ filtering provides an estimate to solve uncertain problem while there exists uncertainty in system. In this paper, a modified H∞ filter is discussed when the intensity of noise is approximately grasped and a loosely coupled system of SINS/GPS integration is introduced in detail. A loosely coupled system using the modified H∞filter is simulated. Simulation shows the modified H∞ filtering can almost achieve the same accuracy as that of Kalman filtering when noise characteristics is well known, and overcome the conservatism of H∞ filtering.

AB - Kalman filtering provides an optimal estimate in terms of minimum mean square error for a SINS/GPS integrated system, H∞ filtering provides an estimate to solve uncertain problem while there exists uncertainty in system. In this paper, a modified H∞ filter is discussed when the intensity of noise is approximately grasped and a loosely coupled system of SINS/GPS integration is introduced in detail. A loosely coupled system using the modified H∞filter is simulated. Simulation shows the modified H∞ filtering can almost achieve the same accuracy as that of Kalman filtering when noise characteristics is well known, and overcome the conservatism of H∞ filtering.

KW - GPS

KW - Kalman Filter

KW - SINS

UR - http://www.scopus.com/inward/record.url?scp=84902440818&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:84902440818

SP - 1294

EP - 1298

T2 - Asian Simulation Conference 2005, ASC 2005 and the 6th International Conference on System Simulation and Scientific Computing, ICSC 2005

Y2 - 24 October 2005 through 27 October 2005

ER -

A simulation of SINS/GPS integrated system using H_∞ filtering

Abstract

Conference

Keywords

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