Research Papers

Observability and Estimation Error Analysis of the Initial Fine Alignment Filter for Nonleveling Strapdown Inertial Navigation System

[+] Author and Article Information
Seong Yun Cho

IT Convergence Technology Research Laboratory,
Electronics and Telecommunications
Research Institute,
218 Gajeongno, Yuseong-gu,
Daejeon, 305-700, Korea
e-mail: sycho@etri.re.kr

Hyung Keun Lee

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

Hung Kyu Lee

Civil Engineering,
Changwon National University, Sarim-dong,
Uichang-gu, Changwon-si, Gyeongsangnam-do, 641-773, Korea
e-mail: hkyulee@changwon.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 January 4, 2011; final manuscript received July 2, 2012; published online November 7, 2012. Assoc. Editor: YangQuan Chen.

J. Dyn. Sys., Meas., Control 135(2), 021005 (Nov 07, 2012) (9 pages) Paper No: DS-11-1001; doi: 10.1115/1.4007552 History: Received January 04, 2011; Revised July 02, 2012

In this paper, performance of the initial fine alignment for the stationary nonleveling strapdown inertial navigation system (SDINS) containing low-grade gyros is analyzed. First, the observability is analyzed by conducting a rank test of an observability matrix and by investigating the normalized error covariance of the extended Kalman filter based on the ten-state model. The results show that the accelerometer biases on horizontal axes are unobservable. Second, the steady-state estimation errors of the state variables are derived using the observability equation. It is verified that the estimates of the state variables have errors due to the unobservable state variables and nonleveling attitude angles of a vehicle containing the SDINS. Especially, this paper shows that the larger the attitude angles of the vehicle are, the greater the estimation errors are. Finally, it is shown that the performance of the eight-state model excluding the two unobservable state variables is better than that of the ten-state model in the fine alignment by a Monte Carlo simulation.

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Fig. 1

Normalized error covariance during initial fine alignment

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Fig. 2

Simulation configuration

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Fig. 3

Roll angle estimation error

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Fig. 4

Pitch angle estimation error

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Fig. 5

Estimation error of x-axis accelerometer bias

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Fig. 6

Estimation error of y-axis accelerometer bias

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Fig. 7

Estimation error of z-axis accelerometer bias

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Fig. 8

Estimation error of x-axis gyro bias

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Fig. 9

Estimation error of y-axis gyro bias




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