Research Papers

Adaptive Redundant Inertial Measurement Unit/Global Positioning System Integration Filter Structure for Fault Tolerant Navigation

[+] Author and Article Information
Seong Yun Cho

Department of Applied Robotics,
Kyungil University,
50, Gamasil-gil, Hayang-eup, Gyeongsan-si,
Gyeongbuk 712-701, Korea
e-mail: sycho@kiu.kr

Hyung Keun Lee

School of Avionics and Telecommunication, Korea Aerospace University,
100 Hanggongdae gil, Hwajeon-dong,
Goyang-city, Gyeonggi-do 412-791, Korea
e-mail: hyknlee@hau.ac.kr

Chan Gook Park

Department of Mechanical
& Aerospace Engineering,
Seoul National University,
1 Gwanak-ro, Gwanak-gu,
Seoul 151-742, Korea
e-mail: chanpark@snu.ac.kr

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received August 24, 2012; final manuscript received September 16, 2013; published online December 9, 2013. Assoc. Editor: Prashant Mehta.

J. Dyn. Sys., Meas., Control 136(2), 021009 (Dec 09, 2013) (10 pages) Paper No: DS-12-1275; doi: 10.1115/1.4025753 History: Received August 24, 2012; Revised September 16, 2013

This paper proposes a new adaptive filter structure containing two subfilters, a fault detection and isolation (FDI) filter, and a main-filter for redundant inertial measurement unit (RIMU)/global positioning system (GPS) integrated navigation system tolerant toward faults of inertial sensors. The purpose of subfilters is compensation of sensor level bias (SLB) of the RIMU during in-flight alignment for successful FDI processing. Also, that of the main-filter is providing of error compensated navigation solution based on the good FDI result. To achieve these purposes, two coordinate frames, sub-body frame (SBF) and master-body frame (MBF), are defined first. Then, two different error models for the RIMU are formulated in the SBF for sensor level compensation and in the MBF for module level compensation, respectively. Based on the formulated error models, an adaptive filter structure is designed. Some numerical simulations are performed to validate the performance of the proposed adaptive filter structure.

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Fig. 1

Orthogonal-cone configured RIMU

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Fig. 2

Structure of adaptive RIMU/GPS integration filter

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Fig. 3

Simulation trajectory

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Fig. 4

Covariance analysis

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Fig. 5

Bias estimation error in the general case




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