• International Journal of Highway Engineering
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• v.16 no.5
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• pp.59-66
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• 2014

PURPOSES : This study is to predict the Sound Pressure Level(SPL) obtained from the Noble Close ProXimity(NCPX) method by using the Extended Kalman Filter Algorithm employing the taylor series and Linear Regression Analysis based on the least square method. The objective of utilizing EKF Algorithm is to consider stochastically the effect of error because the Regression analysis is not the method for the statical approach. METHODS : For measuring the friction noise between the surface and vehicle's tire, NCPX method was used. With NCPX method, SPL can be obtained using the frequency analysis such as Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) and Constant Percentage Bandwidth(CPB) Analysis. In this research, CPB analysis was only conducted for deriving A-weighted SPL from the sound power level in terms of frequencies. EKF Algorithm and Regression analysis were performed for estimating the SPL regarding the vehicle velocities. RESULTS : The study has shown that the results related to the coefficient of determination and RMSE from EKF Algorithm have been improved by comparing to Regression analysis. CONCLUSIONS : The more the vehicle is fast, the more the SPL must be high. But in the results of EKF Algorithm, SPLs are irregular. The reason of that is the EKF algorithm can be reflected by the error covariance from the measurements.

• Journal of Advanced Navigation Technology
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• v.19 no.5
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• pp.389-394
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• 2015

This paper considers the transfer alignment in the inertial navigation system which has lever-arm and the time delay in the velocity measurement. We suggest a method to improve the performance of the velocity matching. First, we analyze the estimation performance of the velocity matching through the tuning of the two covariance matrices of process noise and measurement noise. Next we provide some maneuvering conditions of the vehicles to improve the estimation performance using the observability analysis. The analysis results are verified using the computer simulations, which show that cruise movements do not provide the azimuth estimation of the vehicles, while east or north accelerating movement can provide.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.101-108
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• 2019

Due to recent improvements in computer processing speed and image processing technology, researches are being actively carried out to combine information from camera with existing GNSS (Global Navigation Satellite System) and dead reckoning. In this study, developed a vision-based positioning assistant algorithm to estimate the distance to the object from stereo images. In addition, GNSS/on-board vehicle sensor/vision based positioning algorithm is developed by combining vision based positioning algorithm with existing positioning algorithm. For the performance analysis, the velocity calculated from the actual driving test was used for the navigation solution correction, simulation tests were performed to analyse the effects of velocity precision. As a result of analysis, it is confirmed that about 4% of position accuracy is improved when vision information is added compared to existing GNSS/on-board based positioning algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.1
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• pp.73-82
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• 2010

TERCOM(TERrain COntour Matching), which is the one of the Terrain Referenced Navigation and used in the cruise missile navigation system, is still under development. In this study, the TERCOM based on area-based matching algorithm and extended Kalman filter is analysed through simulation. In area-based matching, the mean square difference (MSD) and cross-correlation matching algorithms are applied. The simulation supposes that the barometric altimeter, radar altimeter and SRTM DTM loaded on board. Also, it navigates along the square track for 545 seconds with the velocity of 1000km per hour. The MSD and cross-correlation matching algorithms show the standard deviation of position error of 99.6m and 34.3m, respectively. The correlation matching algorithm is appeared to be less sensitive than the MSD algorithm to the topographic undulation and the position accuracy of the both algorithms is extremely depends on the terrain. Therefore, it is necessary to develop an algorithm that is more sensitive to less terrain undulation for reliable terrain referenced navigation. Furthermore, studies on the determination of proper matching window size in long-term flight and the determination of the best terrain database resolution needed by the flight velocity and area should be conducted.