• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.3
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• pp.379-385
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• 2018

Boresight process is to match the misalignment between PNU(Position Navigation Unit) and the reference axis of K-MLRS cage. It is important process to ensure accuracy of K-MLRS. When PNU is removed from cage in the previous alignment procedure, there is a misalignment angle with cage of K-MLRS during reassembly process. Therefore, boresight process is always need to align reference axes between PNU and K-MLRS cage. However, this study has proposed the case alignment process that it enable to correspond to reference axes between ISA (Inertial Sensor Assembly) block and PNU case. So, improved alignment procedure enables to install PNU in the reassembly process without additional boresight process.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.4
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• pp.71-80
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• 2006

Strapdown Inertial navigation systems consist of an inertial sensor assembly(ISA), electronic modules to process sensor data, and a navigation computer to calculate attitude, velocity and position. In the ISA, most gryoscopes such as RLGs and FOGs, have digital output, but typical accelerometers use current as an analog output. For a high precision inertial navigation system, sufficient stability and resolution of the accelerometer board converting the analog accelerometer output into digital data needs to be guaranteed. To achieve this precision, the asymmetric error and A/D reset scale error of the accelerometer board must be properly compensated. If the relation between the acceleration error and the errors of boards are exactly known, the compensation and estimation techniques for the errors may be well developed. However, the A/D Reset scale error consists of a pulse-train type term with a period inversely proportional to an input acceleration additional to a proportional term, which makes it difficult to estimate. In this paper, the effects on the acceleration output for auto-pilot situations and the effects of A/D reset scale errors during horizontal alignment are qualitatively analyzed. The result can be applied to the development of the real-time compensation technique for A/D reset scale error and the derivation of the design parameters for accelerometer board.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.64.1-64
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• 2001

Errors in inertial navigation system(INS) can be divided into two major groups which are system related errors and modeling errors due to approximation and linearization. Measurement noise, calibration, and alignment errors make up the first group, whereas the uncertainties in the gravity vector fall in the second category and are important error source for high quality INS, especially during high altitude and and/or long time missions, when the gravity errors tent to build up. The quality of a medium to high accuracy INS depends on the knowledge of the local gravity field. In this paper, the feasibility of improving airborns INS by use of more accurate gravity model is studied. To make consistent comparisons, WGS-84 parameters are used and ...

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.774-778
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• 2008

The inaccuracy of inertial sensors used in low cost IMU's limits the usage to ARS, at best. Sensor fusion technologies are widely used to overcome this problem. GPS is the most popular secondary sensor, but GPS alone cannot fully compensate the IMU errors in the initial alignment process and rectilinear flights. This paper presents a new concept of aiding the low cost IMU with the sun line of sight vector. The simulation and experimental results in this paper proves that aiding of INS/GPS with the sun line of sight vector increases the observability and improves accuracy remarkably.

• Physical Therapy Korea
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• v.30 no.3
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• pp.174-183
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• 2023

Background: Office workers experience neck or back pain due to poor posture, such as flexed head and forward head posture, during long-term sedentary work. Posture correction is used to reduce pain caused by poor posture and ensures proper alignment of the body. Several assistive devices have been developed to assist in maintaining an ideal posture; however, there are limitations in practical use due to vast size, unproven long-term effects or inconsistency of maintaining posture alignment. We developed a headphone and necklace posture correction system (HANPCS) for posture correction using an inertial measurement unit (IMU) sensor that provides visual or auditory feedback. Objects: To demonstrate the test-retest reliability and concurrent validity of neck and upper trunk flexion measurements using a HANPCS, compared with a three-dimensional motion analysis system (3DMAS). Methods: Twenty-nine participants were included in this study. The HANPCS was applied to each participant. The angle for each action was measured simultaneously using the HANPCS and 3DMAS. The data were analyzed using the intraclass correlation coefficient (ICC) = [3,3] with 95% confidence intervals (CIs). Results: The angular measurements of the HANPCS for neck and upper trunk flexions showed high intra- (ICC = 0.954-0.971) and inter-day (ICC = 0.865-0.937) values, standard error of measurement (SEM) values (1.05°-2.04°), and minimal detectable change (MDC) values (2.92°-5.65°). Also, the angular measurements between the HANPCS and 3DMAS had excellent ICC values (> 0.90) for all sessions, which indicates high concurrent validity. Conclusion: Our study demonstrates that the HANPCS is as accurate in measuring angle as the gold standard, 3DMAS. Therefore, the HANPCS is reliable and valid because of its angular measurement reliability and validity.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.93-100
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• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• Journal of Advanced Navigation Technology
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• v.26 no.3
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• pp.179-184
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• 2022

The accurate wing flexure model is required to improve the transfer alignment performance of guided weapon system mounted on a wing of fighter aircraft or armed helicopter. In order to solve this problem, mechanical or stochastical modeling methods have been studying, but modeling accuracy is too low to be applied to weapon systems. The deep learning techniques that have been studying recently are suitable for nonlinear. However, operating fighter aircraft for deep-learning modeling to secure a large amount of data is practically difficult. In this paper, it was used to generate amount of flexure data samples that are similar to the actual flexure data. And it was confirmed that generated data is similar to the actual data by utilizing "measures of similarity" which measures how much alike the two data objects are.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.6
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• pp.479-486
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• 2018

In order to ensure reliable navigation performance of a lunar exploration rover, navigation algorithms using additional sensors such as inertial measurement units and cameras are essential on lunar surface in the absence of a global navigation satellite system. Unprecedentedly, Visual Odometry (VO) using a stereo camera has been successfully implemented at the US Mars rovers. In this paper, we estimate the 6-DOF pose of the lunar exploration rover from gray images of a lunar-like terrains. The proposed algorithm estimates relative pose of consecutive images by sparse image alignment based semi-direct VO. In order to overcome vulnerability to non-linearity of direct VO, we add adaptive motion prior weights calculated from a linear function of the previous pose to the optimization cost function. The proposed algorithm is verified in lunar-like terrain dataset recorded by Toronto University reflecting the characteristics of the actual lunar environment.