• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.56.1-56
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• 2001

Initial attitude error is one of the large error sources in the navigation errors of SDINS. And it is important to decide the initial attitude of SDINS. The method, like a self-alignment or a transfer alignment method, is required to a precise INS. If we do not have a precise INS, we should get large attitude error. After performing the initial alignment, a vehicle has the initial attitude error. Therefore, it results in navigation error due to the initial attitude error. But, if we use position information during flight, we could estimate and compensate a vehicle attitude error. So, we can maintain a precise attitude in spite of existing the initial attitude error. Using the uplinked position information from a land-based radar system, the new algorithm estimates the attitude of the SDINS during flight ...

• Proceedings of the KSRS Conference
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• v.2
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• pp.969-972
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• 2006

The long-range High-Frequency (HF) ocean radar system has observed surface velocity field in the upstream of the Kuroshio north of Ishigaki Island and east of Taiwan since 2001. Applying a new method to extract geostrophic velocity component from the HF surface velocity data with the aid of satellite-born wind data, time series of daily surface geostrophic velocity field has been determined. Despite limited width of the study area of the HF radar, analysis of the sea surface height anomaly determined from the satellite altimetry data in a wider area can provide estimated dates of arrival of mesoscale eddies in the study area of the HF radar. Variations of the Kuroshio position and strength are studied in detail for these cases of interaction with mesoscale eddy, although number of occurrence of direct interaction with the Kuroshio in the study area is not statistically enough. For example, when an anticyclonic eddy approaches to the Kuroshio, the Kuroshio axis is found tend to move northward, keeping away from the approaching eddy from the east.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.8
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• pp.566-573
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• 2019

In order to obtain high-quality images by motion compensation in the airborne synthetic aperture radar (SAR), accurate motion sensing in image acquisition section is necessary. Especially, reducing relative position error and discontinuity in motion sensing is important. To overcome the problem, we propose a pre-parametric error modeling (P-PEM) algorithm which is a real-time motion sensing algorithm for the airborne SAR in this paper. P-PEM is an extended version of parametric error modeling (PEM) method which is a motion sensing algorithm to mitigate the errors in the previous work. PEM estimates polynomial coefficients of INS error which can be assumed as a polynomial in the short term. Otherwise, P-PEM estimates polynomial coefficients in advance and uses at image acquisition section. Simulation results show that the P-PEM reduces relative position error and discontinuity effectively in real-time.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.218-232
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• 2022

Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.2
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• pp.199-206
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• 2013

In general, radial velocity of a target can be obtained by acquiring doppler frequency shift in case of a doppler radar, or can be obtained by acquiring range rate in case of a pulse radar. Then radial velocity can be converted to tangential velocity using aspect angle or position variation per unit time. These two ways have the same meaning in physically, but result in different uncertainty finally. In this paper, it is described not only the two transformation procedures to calculate tangential velocity from radar measurement data, but also the result of combined uncertainty comparison between these two procedures.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.1
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• pp.39-44
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• 2010

A method is presented of dynamic determination of mode transition probability for IMM in order to improve the accuracy performance of maneuvering target tracking for air traffic control surveillance processing system under multiple radar environment. It is shown that dynamic determination of mode transition probability based on the time intervals between the data input from multiple radars gives the optimized performance in terms of position estimation accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.507-512
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• 2007

This paper presents a tracking filter with ship's motion compensation for a ship-borne radar tracking system. The ship's maneuver is described by displacement and rotational motions in the ship-centered east-north frame. The first order Taylor series approximation of the measurement error covariance of the converted measurement is derived in the ship-centered east-north frame. The ship's maneuver is compensated by incorporating the measurement error covariance of the converted measurement and displacement of the position state in the tracking filter. The simulation results via 500 Monte-Carlo runs show that the proposed method follows the target successfully and provides consistent tracking performance during ship's maneuvers while the conventional tracking filter without ship motion compensation fails to track during such periods.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.223-229
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• 2017

An angle error is a factor obstructing to track accurate position in tracking radars. And the noise incurring the angle error can be divided as follows; thermal noise and glint. In general, Extended Kalman filter used in tracking radars is designed with considering thermal noise only. The Extended Klaman filter uses a fixed measurement error covariance when updating an estimate state by using ahead state and measurement. But, a noise power varies according to the range. Therefore we purposes the adaptive Kalman filter which changes the measurement noise covariance according to the range. In this paper, we compare the performance of the Extended Kalman filter and the proposed adaptive Kalman filter by considering KSLV-I (Korean Satellite Launch Vehicles).

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.262-267
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• 1998

In Synthetic Aperture Radar(SAR) imaging, the echoed data are collected by moving radar's position with respect to the target area, and this operation actually gives effect of synthesizing aperture size, which in turn gives better cross range resolution of reconstructed target scene. Among several inversion scheme for SAR Imaging, we uses an inversion scheme which uses no approximation in wave propagation analysis, and try to verify whether the collected data with synthesized aperture actually gives the same support as that with physical aperture in the same size. To do this, we make a signal subspace comparison of two imaging models with physical and synthesized arrays, respectively. Theoretical comparison and numerical analysis using Gram-Schmidt procedures had been performed. The results showed that the synthesized array data fully span the physical array data with the same system geometry and strongly support the proposed inversion scheme valuable in high resolution radar imaging.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.517-523
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• 2011

The estimation filter in radar systems must track targets' position within low tracking error. In the Multi-Function Radar(MFR), ${\alpha}-{\beta}$ filter and Kalman filter are widely used to track single or multiple targets. However, due to target maneuvering, these filters may not reduce tracking error, therefore, may lost target tracks. In this paper, a target tracking filter based on particle filtering algorithm is proposed for the MFR. The advantage of this method is that it can track targets within low tracking error while targets maneuver and reduce impoverishment of particles by the proposed resampling method. From the simulation results, the improved tracking performance is obtained by the proposed filtering algorithm.