• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.856-859
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• 1996

In this paper, a coarse alignment algorithm for strapdown inertial navigation system is proposed and evaluated analytically. The algorithm computes roll and pitch angles of the vehicle using accelerometer outputs, and then determines yaw angle with gyro outputs. It is referred, as two-step coarse alignment in this work. With the geometric relation between sensor outputs and roll, pitch and yaw angles, the algorithm error is analytically derived and compared with the previous coarse alignment algorithm introduced by Britting. The results show that the proposed two-step coarse alignment algorithm has better performance for pitch angle computation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.177.3-177
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• 2001

In this paper, the design of the in-motion alignment system of Strapdown Inertial Navigation System(SDINS) using Robust Extended Kalman Filter(REKF) is presented. The compensation of errors in the aided navigation system is accomplished by the indirect feedback filtering. The performance of the aided navigation algorithm is very sensitive to the accuracy of the initial estimate, which is the characteristic of the EKF. Unfortunately, the initial attitude error can be very large during the in-motion alignment. To overcome the in-motion alignment under large initial attitude error problem, the REKF using linear robust filtering technique is proposed. The linear robust H$_2$ filter can be adopted for nonlinear ...

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.11
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• pp.1124-1129
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• 2006

In this paper, in-flight alignment algorithm using UKF is presented for an SDINS aided by SSBL or GPS system under large initial heading error. The EKF usually applied for this task. This approximates the propagation of mean and covariance accurate to first-order only. To overcome this limitation, the unscented transformation that achieves second order approximation is applied to the in-flight alignment. To analyze the performance of the proposed method, simulations for S-type trajectory are carried out. The results show that performance of EKF and UKF are the almost same when the initial heading error is smaller than $30^{\circ}$, but UKF has a better performance for large initial heading error about $45^{\circ}$.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.6
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• pp.463-472
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• 2017

Inertial Navigation System Alignment is the process to determine direction cosine matrix which is the transformation matrix between the INS body frame and navigation frame. INS initial position value is necessary to INS attitude calculation, so that user should wait until he get such value to start the INS alignment. To remove the waiting time, we propose an alignment algorithm that immediately starts after the INS power on by using pseudo initial position input and then is completed with attitude error compensation by entering true position later. We analyse effect of INS sensor error on attitude in process of time and verify the performance and usefulness of the close-loop alignment algorithm which corrects attitude error from the change of initial position.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.4
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• pp.435-442
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• 2016

For inertial navigation systems, initial information such as position, velocity and attitude is required for navigation. Self-alignment is the process to determine initial attitude on stationary condition using inertial measurements such as accelerations and angular rates. The accuracy of self-alignment is determined by inertial sensor error. As soon as an inertial navigation system is powered on, the temperature of accelerometer rises rapidly until temperature stabilization. It causes acceleration error which is called temperature stabilizing error of accelerometer. Therefore, temperature stabilizing error degrades the alignment accuracy and also increases alignment time. This paper suggests a method to calculate azimuthal attitude using curve fitting of horizontal control angular rate in fixed-gain self-alignment. It is verified by simulation and experiment that the accuracy is improved and the alignment time is reduced using the proposed method under existence of the temperature stabilizing error.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.8
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• pp.796-803
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• 2011

It is called self-alignment or initial alignment that INS(Inertial Navigation System) is aligned using the measurements from the inertial sensors as an accelerometer and a gyroscope and the inserted reference navigation information in the stop state. The main purpose of self-alignment is to obtain the initial attitude of INS. The accuracy of self-alignment is determined by the performance grade of the used inertial sensors, especially horizontal attitude accuracy by the horizontal accelerometer and vertical attitude accuracy by the E-axis gyroscope. Therefore the uncertain errors in the inertial sensors cause the performance of self-alignment to degrade. In this paper, we analyze theoretically and through a simulation how the errors of inertial sensors in the temperature stabilizing state, one of the uncertain errors, affect the accuracy of self-alignment.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.195-204
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• 2022

In this paper, we described the rapid initial alignment techniques of projectile navigation system for use in inclined launch systems. One-shot alignment technique, one of the rapid initial alignment techniques, is possible to align a navigation system within seconds because it uses external information from an launcher navigation system. However, since it has only been used in vertical launch systems, its performance in inclined launch systems has not been verified. Therefore, this paper analyzed the error elements that occur when the one-shot alignment technique is applied to the inclined launch system, and introduced a method to improve the alignment performance by minimizing those errors. Additionally, By simulating and testing the performance of the proposed alignment technique, it was verified that it is effective even in an environment where a real navigation system is used.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.468-473
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• 2014

This paper describes the in-flight alignment of SDINS (Strapdown Inertial Navigation Systems) using an EKF (Extended Kalman Filter) and a UKF (Unscented Kalam Filter), which allow large initial attitude error uncertainty. Regardless of the inertial sensors, there are nonlinear error dynamics of SDINS in cases of large initial attitude errors. A UKF that is one of the nonlinear filtering approaches for IFA (In-Flight Alignment) are used to estimate the attitude errors. Even though the EKF linearized model makes velocity errors when predicting incorrectly in case of large attitude errors, a UKF can represent correctly the velocity errors variations of attitude errors with nonlinear attitude error components. Simulation results and analyses show that a UKF works well to handle large initial attitude errors of SDINS and the alignment error attitude estimation performance are quite improved.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.10
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• pp.836-843
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• 2017

In lunar exploration, the necessity of utilizing rover is verified by the examples of the Soviet Union and China and the similar Mars missions of the United States. In order to achieve the successful management of a lunar rover, a high precision navigation technique is required, and accordingly, high precision initial alignment is essential. Even though it is general to perform initial alignment in a steady state, a multiposition alignment technique is applied when high performance is needed. On the lunar surface, however, the performance of initial alignment decreases from that on Earth, and it cannot be improved by applying multiposition alignment method owing to certain constraints of lunar environment. In this paper, a sun sensor aided multiposition alignment technique is proposed. The measurement model for a sun vector is established, and its observability analysis is performed. The performance of the proposed algorithm is verified through computer simulations, and the results show the estimation performance is improved dramatically.