• International Journal of Aeronautical and Space Sciences
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• v.3 no.1
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• pp.86-94
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• 2002

The initial alignment of inertial platform for navigation system was considered. An adaptive filtering technique is developed for the system with unknown and varying sway disturbance. It is assumed that the random sway motion is the second order ARMA(Auto Regressive Moving Average) model and performed parameter identification for unknown parameters. Designed adaptive filter contain both a Kalman filter and a self-tuning filter. This filtering system can automatically adapt to varying environmental conditions. To verify the robustness of the filtering system, the computer simulation was performed with unknown and varying sway disturbance.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.265-270
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• 2006

Recently, inertial sensors like gyros and accelerometers have been quite miniaturized by Micro Electro-Mechanical Systems (MEMS) technology. JAXA is developing a MEM-based GPS/INS hybrid navigation system named Micro-GAIA. The navigation performance of Micro-GAIA was evaluated through off-line analysis by using flight test data. The estimation errors of the roll, pitch, and azimuth were $0.03^{\circ}$, $0.05^{\circ}$, $0.05^{\circ}$ $(1{\sigma})$, respectively. he horizontal position errors after 60-second GPS outages were reduced to 25 m CEP. The attitude errors and position errors are nearly half of ones reported previously[2]. Furthermore, using the adaptive Kalman filters, the robustness against the uncertainty of the measurement noise was improved. Comparing the innovation-based and residual-based adaptive Kalman filters, it was confirmed that the latter is robuster than the former.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.1
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• pp.24-36
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• 2011

A new approach for spacecraft absolute attitude estimation based on the unscented Kalman filter (UKF) is extended to relative attitude estimation and navigation. This approach for nonlinear systems has faster convergence than the approach based on the standard extended Kalman filter (EKF) even with inaccurate initial conditions in attitude estimation and navigation problems. The filter formulation employs measurements obtained from a vision sensor to provide multiple line(-) of(-) sight vectors from the spacecraft to another spacecraft. The line-of-sight measurements are coupled with gyro measurements and dynamic models in an UKF to determine relative attitude, position and gyro biases. A vector of generalized Rodrigues parameters is used to represent the local error-quaternion between two spacecraft. A multiplicative quaternion-error approach is derived from the local error-quaternion, which guarantees the maintenance of quaternion unit constraint in the filter. The scenario for bounded relative motion is selected to verify this extended application of the UKF. Simulation results show that the UKF is more robust than the EKF under realistic initial attitude and navigation error conditions.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.565-573
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• 2017

This paper presents a multistage in-flight alignment (MIFA) method for a strapdown inertial navigation system (SDINS) suitable for moving vehicles with no initial attitude references. A SDINS mounted on a moving vehicle frequently loses attitude information for many reasons, and it makes solving navigation equations impossible because the true motion is coupled with an undefined vehicle attitude. To determine the attitude in such a situation, MIFA consists of three stages: a coarse horizontal attitude, coarse heading, and fine attitude with adaptive Kalman navigation filter (AKNF) in order. In the coarse horizontal alignment, the pitch and roll are coarsely estimated from the second order damping loop with an input of acceleration differences between the SDINS and GPS. To enhance estimation accuracy, the acceleration is smoothed by a scalar filter to reflect the true dynamics of a vehicle, and the effects of the scalar filter gains are analyzed. Then the coarse heading is determined from the GPS tracking angle and yaw increment of the SDINS. The attitude from these two stages is fed back to the initial values of the AKNF. To reduce the estimated bias errors of inertial sensors, special emphasis is given to the timing synchronization effects for the measurement of AKNF. With various real flight tests using an UH60 helicopter, it is proved that MIFA provides a dramatic position error improvement compared to the conventional gyro compass alignment.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.115-120
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• 2006

This article propose a new idea of developing a hybrid scheme to achieve faster INS alignment with higher accuracy using a novel procedure to estimate the initial attitude angles that combines a Kalman filter and Adaptive Neuro-Fuzzy Inference System architecture. A tactical grade inertial measurement unit was applied to verify the performance of proposed scheme in this study. The preliminary results indicated the outstanding improvements in both time consumption for fine alignment process and accuracy of estimated attitude angles, especially in heading angles. In general, the improvement in terms of time consumption and the accuracy of estimated attitude estimated accuracy reached 80% and 70% respectively during alignment process after compensating the attitude angles estimated by an extended Kalman filter with 15 states using proposed approach. It is worth mentioned that the proposed approach can be implemented in general real time navigation applications.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.355-359
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• 2006

For the poor observability of azimuth misalignment angle and east gyro drift rate of the traditional initial alignment, a bran-new SINS stationary fast self-alignment approach is proposed. By means of analyzing the characteristic of the strapdown inertial navigation system (SINS) stationary alignment seriously, the new approach takes full advantage of the specific force and angular velocity information given by inertial measurement unit (IMU) instead of the mechanization of SINS. Firstly, coarse alignment algorithm is presented. Secondly, a new fine alignment model for SINS stationary self-alignment is derived, and the observability of the model is analysed. Then, a modified Sage-Husa adaptive Kalman filter is introduced to estimate the misalignment angles. Finally, some computer simulation results illustrate the efficiency of the new approach and its advantages, such as higher alignment accuracy, shorter alignment time, more self-contained and less calculation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.11
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• pp.1079-1086
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• 2008

During transfer alignment navigation information transferred MINS(master inertial navigation system) to SINS(slave inertial navigation system) has a changed time delay. The changed time delay degrades the performance of transfer alignment. This paper proposes an adaptive filter that estimates covariance of a time delay in real-time using residual of measurements. The performance of the adaptive filter is compared with that of the EKF(extended Kalman filter) in case of transfer alignment for vertical launcher in the ship. The results show that proposed method is more effective than EKF in estimating attitude errors.

• Journal of Advanced Navigation Technology
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• v.3 no.2
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• pp.97-107
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• 1999

Passive sensors such as Laser Range Finder(LRF) and Forward Looking Infrared(FLIR) camera frequently used for tracking aircraft landing produce the measurements of elevation angle contaminated by large noise due to the exhaust plume disturbance. This results in poor tracking performance if the extended Kalman filter is used for estimation of the range and elevation which are corrupted by the non-Gaussian noise such as plume disturbance. In this paper, an adaptive estimation filter and the extended Kalman filter is combined to produce a combination-type filter. In this approach the adaptive filter is used for the plume-type disturbance noise and the extended Kalman filter is utilized for the measurement of Gaussian type. The proposed combination filter is effective for the trajectory estimation of landing aircraft under the influence of unknown bias and numerical simulations illustrate the performance of the proposed filter.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.1
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• pp.14-21
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• 2007

This paper proposes SDINS(strapdown inertial navigation system) transfer alignment method for vertical launcher using an adaptive filter in the ship. First, the velocity and attitude matching transfer alignment method is designed to align SDINS for vertical launcher. Second, the adaptive filter is employed to estimate measurement noise variance in real time using the residual of measurements. Because it is difficult to decide measurement noise variance when noise properties of the ship SDINS are changed. To verify its performance, it is compared with the EKF(Extended Kalman filter) using uncorrect measurement variance. The monte carlo simulation results show that proposed method is more effective in estimating attitude angle than EKF.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.555-564
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• 2017

In this paper, a new technique for attitude and heading reference system (AHRS) using low-cost MEMS sensors of the gyroscope, accelerometer, and magnetometer is addressed particularly in vibration environments. The motion of MEMS sensors interact with the scale factor and cross-coupling errors to produce random errors by the harsh environment. A new adaptive attitude estimation algorithm based on the Kalman filter is developed to overcome these undesirable side effects by analyzing windowed measurement error covariance. The key idea is that performance degradation of accelerometers, for example, due to linear vibrations can be reduced by the proposed measurement error covariance analysis. The computed error covariance is utilized to the measurement covariance of Kalman filters adaptively. Finally, the proposed approach is verified by using numerical simulations and experiments in an acceleration phase and/or vibrating environments.