• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2270-2273
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• 2001

This paper presents calibration and alignment algorithms for low-cost inertial sensors. The error models for gyro and accelerometer are presented with a study of their effects. A navigational Kalman Filter is derived based on those error models. Test results are presented, which shows the initial calibration and alignment scheme and the proposed filter configuration effectively reduce the drift of the sensors and provide improved accuracy for its practical use for navigation.

• 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.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.16-23
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• 2020

This paper describes an alignment algorithm that estimates the initial heading angle of AUVs (Autonomous Underwater Vehicle) for starting navigation in a sea area. In the basic dead reckoning system, the initial orientation of the vehicle is very important. In particular, the initial heading value is an essential factor in determining the performance of the entire navigation system. However, the heading angle of AUVs cannot be measured accurately because the DCS (Digital Compass) corrupted by surrounding magnetic field in pointing true north direction of the absolute global coordinate system (not the same to magnetic north direction). Therefore, we constructed an experimental constraint and designed an algorithm based on extended Kalman filter using only inertial navigation sensors and a GPS (Global Positioning System) receiver basically. The value of sensor covariance was selected by comparing the navigation results with the reference data. The proposed filter estimates the initial heading angle of AUVs for navigation in a sea area and reflects sampling characteristics of each sensor. Finally, we verify the performance of the filter through experiments.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.12
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• pp.1742-1747
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• 2015

An improved transfer alignment method for a strap-down inertial navigation system (SDINS) is presented here. The alignment accuracy in conventional method is vulnerable to the data latency of a Master INS (MINS) in high maneuverable platforms. We propose a time delay compensation equation considering higher-order terms in the attitude measurement equation of the Kalman filter. The equation incorporates additional information including angular rate, angular acceleration and linear acceleration from the MINS. Simulation results show that the transfer alignment accuracy is significantly improved in the high dynamic environment by incorporating the latency compensation technique.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.4
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• pp.708-715
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• 2010

In Strapdown Inertial Navigation System, alignment accuracy is the most important factor to determine the performance of navigation. However by an existing self-alignment method, it takes a long time to acquire the alignment accuracy that we want. So, to attain the desired alignment accuracy in as little as $\bigcirc$ minutes, we have developed the precise multi-position alignment method. In this paper, it is proposed a inertial measurement matching transfer alignment method among alignment methods to minimize the alignment error in a short time. It is based on a mixed velocity-DCM matching method be suitable to the operating environment of vertical launching system. The compensation methods to reduce misalign error, especially azimuth angle error incurred by measurement time-delay error and body flexure error are analyzed and evaluated with simulation. This simulation results are finally confirmed by experimentations using FMS(Flight Motion Simulator) in Lab and the integration test to follow the fire control mission.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.676-679
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• 1996

In this paper, the uniform observability and the error characteristics for stationary SDINS error are analyzed. The use of the Lyapunov transformation is proposed for transforming te conventional SDINS error model and the sufficient conditions for the uniform observability of SDINS error model are analytically derived. A complete characterization for the SDINS error characteristics during two position alignment is presented which allows us to predict the performance of two position alignment in SDINS.

• 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 Positioning, Navigation, and Timing
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• v.6 no.4
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• pp.167-179
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• 2017

In this paper, a simplified Cubature Kalman Filter (SCKF) is proposed to reduce the computation load of CKF, which is then used as a filter for transfer alignment of shipboard INS. CKF is an approximate Bayesian filter that can be applied to non-linear systems. When an initial estimation error is large, convergence characteristic of the CKF is more stable than that of the Extended Kalman Filter (EKF), and the reliability of the filter operation is more ensured than that of the Unscented Kalman Filter (UKF). However, when a system degree is large, the computation amount of CKF is also increased significantly, becoming a burden on real-time implementation in embedded systems. A simplified CKF is proposed to address this problem. This filter is applied to shipboard inertial navigation system (INS) transfer alignment. In the filter design for transfer alignment, measurement type and measurement update rate should be determined first, and if an application target is a ship, lever-arm problem, flexure of the hull, and asynchronous time problem between Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS) should be taken into consideration. In this paper, a transfer alignment filter based on SCKF is designed by considering these problems, and its performance is validated based on simulations.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.3
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• pp.184-192
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• 2010

In this paper, an initial fine alignment algorithm, which is developed for the strap-down inertial navigation systems of satellite launch vehicles, is considered. For fast and accurate alignment, a simple closed-loop estimation algorithm using a proportional-integral controller is introduced. Through computer simulation for the sway condition in the launch pad, it is shown that a simple filter structure can guarantee fast computational speed that is adequate for real-time implementation as well as the required alignment accuracy and robustness. In addition, its implementation results are presented for the Naro-1 flight test.