Initial Leveling of Strapdown Inertial Navigation System with an On-Line Robust Input Estimator

Sou-Chen LEE; Cheng-Yu LIU

Initial Leveling of Strapdown Inertial Navigation System with an On-Line Robust Input Estimator

Sou-Chen LEE, Cheng-Yu LIU

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Initial leveling of strapdown inertial navigation system is a prerequisite work for distinguishing between gravity and acceleration effects in the accelerometer sensing's. This study presents an on-line methodology to resolve the initial leveling problem of a vehicle, which is subject to a large, long duration, and abrupt disturbance input with a deterministic nature under noisy circumstances. The developed method herein is the Kalman filter based scheme with a robust input estimator, generalized M estimator, and a testing criterion. The generalized M estimator identifies the unexpected disturbance inputs in real time. In addition, hypothetical testing based on the least-squares estimator is devised to detect the input's onset and presence. A required regression equation between the observed value of the residual sequence with an unknown input and theoretical residual sequence of the Kalman filter with no input is formulated. Input estimation and detection are then provided on the basis of the derived regression equation. Moreover, Monte Carlo simulations are performed to assess the superior capabilities of the proposed method in term of rapid responses, accuracy, and robustness. The efficient initial leveling can facilitate the entire alignment of the inertial system.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E81-A No.11 pp.2383-2390

Publication Date: 1998/11/25

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Category: Digital Signal Processing

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Sou-Chen LEE, Cheng-Yu LIU, "Initial Leveling of Strapdown Inertial Navigation System with an On-Line Robust Input Estimator" in IEICE TRANSACTIONS on Fundamentals, vol. E81-A, no. 11, pp. 2383-2390, November 1998, doi: .
Abstract: Initial leveling of strapdown inertial navigation system is a prerequisite work for distinguishing between gravity and acceleration effects in the accelerometer sensing's. This study presents an on-line methodology to resolve the initial leveling problem of a vehicle, which is subject to a large, long duration, and abrupt disturbance input with a deterministic nature under noisy circumstances. The developed method herein is the Kalman filter based scheme with a robust input estimator, generalized M estimator, and a testing criterion. The generalized M estimator identifies the unexpected disturbance inputs in real time. In addition, hypothetical testing based on the least-squares estimator is devised to detect the input's onset and presence. A required regression equation between the observed value of the residual sequence with an unknown input and theoretical residual sequence of the Kalman filter with no input is formulated. Input estimation and detection are then provided on the basis of the derived regression equation. Moreover, Monte Carlo simulations are performed to assess the superior capabilities of the proposed method in term of rapid responses, accuracy, and robustness. The efficient initial leveling can facilitate the entire alignment of the inertial system.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e81-a_11_2383/_p

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@ARTICLE{e81-a_11_2383,
author={Sou-Chen LEE, Cheng-Yu LIU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Initial Leveling of Strapdown Inertial Navigation System with an On-Line Robust Input Estimator},
year={1998},
volume={E81-A},
number={11},
pages={2383-2390},
abstract={Initial leveling of strapdown inertial navigation system is a prerequisite work for distinguishing between gravity and acceleration effects in the accelerometer sensing's. This study presents an on-line methodology to resolve the initial leveling problem of a vehicle, which is subject to a large, long duration, and abrupt disturbance input with a deterministic nature under noisy circumstances. The developed method herein is the Kalman filter based scheme with a robust input estimator, generalized M estimator, and a testing criterion. The generalized M estimator identifies the unexpected disturbance inputs in real time. In addition, hypothetical testing based on the least-squares estimator is devised to detect the input's onset and presence. A required regression equation between the observed value of the residual sequence with an unknown input and theoretical residual sequence of the Kalman filter with no input is formulated. Input estimation and detection are then provided on the basis of the derived regression equation. Moreover, Monte Carlo simulations are performed to assess the superior capabilities of the proposed method in term of rapid responses, accuracy, and robustness. The efficient initial leveling can facilitate the entire alignment of the inertial system.},
keywords={},
doi={},
ISSN={},
month={November},}

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TY - JOUR
TI - Initial Leveling of Strapdown Inertial Navigation System with an On-Line Robust Input Estimator
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2383
EP - 2390
AU - Sou-Chen LEE
AU - Cheng-Yu LIU
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E81-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 1998
AB - Initial leveling of strapdown inertial navigation system is a prerequisite work for distinguishing between gravity and acceleration effects in the accelerometer sensing's. This study presents an on-line methodology to resolve the initial leveling problem of a vehicle, which is subject to a large, long duration, and abrupt disturbance input with a deterministic nature under noisy circumstances. The developed method herein is the Kalman filter based scheme with a robust input estimator, generalized M estimator, and a testing criterion. The generalized M estimator identifies the unexpected disturbance inputs in real time. In addition, hypothetical testing based on the least-squares estimator is devised to detect the input's onset and presence. A required regression equation between the observed value of the residual sequence with an unknown input and theoretical residual sequence of the Kalman filter with no input is formulated. Input estimation and detection are then provided on the basis of the derived regression equation. Moreover, Monte Carlo simulations are performed to assess the superior capabilities of the proposed method in term of rapid responses, accuracy, and robustness. The efficient initial leveling can facilitate the entire alignment of the inertial system.
ER -