• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.222-231
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• 2016

This paper proposes a new method achieving computationally efficient attitude reference system for low cost strapdown sensors and microprocessor platform. The main idea in this method is to define and compare velocity differential vectors, geometrically computed from INS and GPS data with different update rate, for generating attitude error measurements which is further used for filter construction. A quaternion based Kalman filter configuration is applied for the attitude estimation with the adapted measurement model of differential vector comparison. Linearized model for Extended Kalman Filter and low pass filtered characteristics of measurement greatly extend the affordability of the proposed algorithm to the field of simple low cost embedded systems. For performance verification, experiment are done employing a practical low cost MEMS IMU and GPS receiver specification. Performance comparison with a high grade navigation system demonstrated good estimation result.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.369-378
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• 2013

This paper presents a vision/LiDAR integrated navigation system that provides accurate relative navigation performance on a general ground surface, in GNSS-denied environments. The considered ground surface during flight is approximated as a piecewise continuous model, with flat and slope surface profiles. In its implementation, the presented system consists of a strapdown IMU, and an aided sensor block, consisting of a vision sensor and a LiDAR on a stabilized gimbal platform. Thus, two-dimensional optical flow vectors from the vision sensor, and range information from LiDAR to ground are used to overcome the performance limit of the tactical grade inertial navigation solution without GNSS signal. In filter realization, the INS error model is employed, with measurement vectors containing two-dimensional velocity errors, and one differenced altitude in the navigation frame. In computing the altitude difference, the ground slope angle is estimated in a novel way, through two bisectional LiDAR signals, with a practical assumption representing a general ground profile. Finally, the overall integrated system is implemented, based on the extended Kalman filter framework, and the performance is demonstrated through a simulation study, with an aircraft flight trajectory scenario.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.2
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• pp.128-136
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• 2011

An approach to improve the performance of SDINS and GPS integrated system for bank-to-turn flight vehicles is described. Then, it is shown through the simulation that a specific gyro misalignment error results in an increased heading error of SDINS. A new modelling method is presented herein for identifying of sensor and attitude error. The main advantage of the proposed method is that it not only estimates the gyro misalignment error of SDINS, but also improves estimate performance of heading error of SDINS in the presence of the gyro misalignments.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.543-549
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• 2021

In this paper, a robust transfer alignment method is proposed for a strapdown inertial navigation system(SDINS) with norm-bounded parametric uncertainties. The uncertainties are described by the energy bound constraint, i.e., sum quadratic constraint(SQC). It is shown that the SQC can be coverted into an indefinite quadratic cost function in the Krein space. Krein space Kalman filter is designed by modifying the measurement matrix and the variance of measurement noises in the conventional Kalman filter. Since the proposed Krein space Kalman filter has the same recursive structure as a conventional Kalman filter, the proposed filter can easily be designed. The simulation results show that the proposed filter achieves robustness against measurement time delay and high dynamic environment of the vehicle.