Gyrocompassing mode of the attitude and heading reference system

Avrutov, V. V.; Buhaiov, D. V.; Meleshko, Vladislav

doi:10.1109/apuavd.2017.8308793

Cited by 12 publications

(3 citation statements)

References 15 publications

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“…At present, the research about north-seeker includes basic inertial navigation error model [ 1 , 2 ], observability analysis [ 3 , 4 ], in-drilling alignment with general dynamic error model [ 5 ], alignment algorithms based on gyro-compassing mode [ 6 , 7 ], alignment based on the interacting multiple model and the Huber methods [ 8 ], rapid fine alignment under marine mooring condition [ 9 ], alignment for SINS (strapdown inertial navigation system) in vehicular environment [ 10 ], alignment based on Riccati Equation and EM (expectation-maximization) convergence [ 11 ], alignment based on adjustment on separate-bias Kalman filter [ 12 ], application of nonlinear filtering in alignment [ 13 ], initial attitude estimation of tactical grade inertial measurement [ 14 ], alignment with robust adaptive unscented Kalman filter [ 15 ], alignment based on a group of double direct spatial isometries [ 16 ], alignment with state-dependent extended Kalman filter [ 17 ], application of redundant technology in north-finding [ 18 ], two-position algorithm [ 19 , 20 ], multi-position algorithm [ 21 , 22 ], rotary-modulation algorithm [ 23 , 24 , 25 ], nonlinear filter model for large misalignment angle [ 26 ], transfer north-seeking algorithm [ 27 , 28 , 29 ], transfer alignment based on cubature Kalman filter (CKF) method [ 30 ], north-finding based on the neural network technology [ 31 , 32 ], transfer algorithm based on sensors network and estimation of wing flexure deformation [ 33 ], fast stationary initial alignment based on extended measurement information [ 34 ], accurate fine alignment based on adaptive extended Kalman filters [ 35 ], compact north-seeker technology [ 36 ], north-seeking based on the information fusion technology [ ...…”

Section: Introductionmentioning

confidence: 99%

A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)

Bai

Zhang

et al. 2022

Sensors

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We propose a kind of fast and high-precision alignment algorithm based on the ESO technology. Firstly, in order to solve the problems of rapid, high-accuracy, and anti-interference alignment on the moving pedestal in the north-seeker, the ESO technology in control theory is introduced to improve the traditional Kalman fine-alignment model. This method includes two stages: the coarse alignment in the inertial frame and fine alignment based on the ESO technology. By utilizing the ESO technology, the convergence speed of the heading angle can be greatly accelerated. The advantages of this method are high-accuracy, fast-convergence, strong ability of anti-interference, and short time-cost (no need of KF recursive calculation). Then, the algorithm model, calculation process, and the setting initial-values of the filter are shown. Finally, taking the shipborne north-finder based on the FOG (fiber-optic gyroscope) as the investigated subject, the test on the moving ship is carried out. The results of first off-line simulation show that the misalignment angle of the heading angle of the proposed (traditional) method is ≤2.1′ (1.8′) after 5.5 (10) minutes of alignment. The results of second off-line simulation indicate that the misalignment angle of the heading angle of the proposed (traditional) method is ≤4.8′ (14.2′) after 5.5 (10) minutes of alignment. The simulations are based on the ship-running experimental data. The measurement precisions of Doppler velocity log (DVL) are different in these two experiments.

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Section: Introductionmentioning

confidence: 99%

A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)

Bai

Zhang

et al. 2022

Sensors

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“…І н ф о р м а ц і й н і с и с т е м и , м е х а н і к а т а к е р у в а н н я Запишемо формулу(7) через різницю довготи…”

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Position initial values determination by means of inertial technology

Avrutov¹

2019

ISMC

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“…GM can be used to estimate preliminary values for GM parameters (used in G matrix) by processing long stationary record of gyroscope measurements. However, as will be seen in chapter (4), the preliminary values of estimated in this way are not guaranteed to lead to the best EKF performance [55,59,60]. As also explained in the previous section, the value of R represents the noise of the tilt/heading updates calculated from accelerometer/magnetometer.…”

Section: Ahrs-ekf Q and R Parameters Estimation Problemmentioning

confidence: 99%

Multi-Sensor Attitude and Heading Reference System Design Using Genetically Optimized Kalman Filter

Gessesse¹

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Attitude and Heading Reference System (AHRS) is a self-contained sensors assembly that can estimate full 3D orientation of an object. 3D orientation estimation is of great importance in many applications such as robot navigation, augmented/virtual reality, and mobile mapping. The AHRS system model involves integration of angular rate measurements from gyroscope which are fused with absolute measurements from magnetometer/accelerometer using Extended Kalman Filter (EKF). EKF accuracy is greatly affected by process noise parameters and measurement noise parameters. Therefore, this thesis developed a systematic method of EKF noise parameters optimization using a hybrid stochastic, Genetic Algorithms (GA)-based approach supported by Design of Experiments (DoE) technique. Noise parameters are partially obtained using stochastic modeling methods such as Gauss-Markov (GM) and Allan Variance (AV). Then, further optimization is performed using GA and DoE methods. The proposed approach has been developed in MATLAB and tested on simulation data and verified on real data collected under different scenarios. Results showed that the proposed approach can provide 40-60% better accuracy compared to conventional methods within few GA iterations. In addition, application of DoE technique reduces GA iterations to convergence by approximately 60%.

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Gyrocompassing mode of the attitude and heading reference system

Cited by 12 publications

References 15 publications

A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)

A Fast North-Finding Algorithm on the Moving Pedestal Based on the Technology of Extended State Observer (ESO)

Position initial values determination by means of inertial technology

Multi-Sensor Attitude and Heading Reference System Design Using Genetically Optimized Kalman Filter

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