• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.43-46
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• 2000

In this paper, we present a method for vehicle tracking systems using $\alpha$-$\beta$ filter based on fuzzy logic. The $\alpha$-$\beta$ filter estimates the future target positions using fixed $\alpha$.$\beta$ coefficients. We utilize the fuzzy logic to make $\alpha$ and $\beta$ coefficients very with the position. Comparisons of tracking performance made for three different schemes: the $\alpha$-$\beta$ filter, $\alpha$-$\beta$filter using fuzzy logic, and the kalman filter.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.145-147
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• 2017

Tracking filter plays a key role in accurate estimation and prediction of maneuvering vessel's dynamics. The third-order ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. Fading memory algorithm performs a better performance in numerous of ${\alpha}-{\beta}-{\gamma}$ filter algorithms. This study aims to optimize the fourth-order fading memory algorithm ${\alpha}-{\beta}-{\gamma}-{\eta}$ filter, which is extended form ${\alpha}-{\beta}-{\gamma}$ filter, to get much more accurate position of high dynamic target on the condition that the own ship is also high dynamic.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.154-157
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• 2000

In this paper, we propose a method for vehicle tracking systems using fuzzy logic. The standard ${\alpha}$-${\beta}$ filter estimates the future target positions using fixed ${\alpha}$,${\beta}$ coefficients. We utilize the if-then fuzzy logic to make ${\alpha}$ and ${\beta}$ coefficients vary with the position. Comparisons are made in tracking vehicles using three different schemes: the standard ${\alpha}$-${\beta}$ filter, ${\alpha}$-${\beta}$ filter using fuzzy logic, and the Kalman filter.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.16-24
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• 2011

Automotive Radar Systems are currently under development for various applications to increase accuracy and reliability. The target tracking is most important in single or multiple target environments for accuracy. The tracking algorithm provides smoothed and predicted data for target position and velocity(Doppler). To this end, the fixed gain filter(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter) and dynamic filter(Kalman filter, Singer-Kalman filter, etc) are commonly used. Gating is used to decide whether an observation is assigned to an existing track or new track. Gating algorithms are normally based on computing a statistical error distance between an observation and prediction. The data association takes the observation-to-track pairings that satisfied gating and determines which observation-to-track assignment will actually be made. For data association, NNPDA(Nearest Neighbor Probabilistic Data Association) algorithm is proposed. In this paper, we designed a target tracking system developed for an Automotive Radar System. We show the experimental results of the 77GHz FMCW radar sensor on the roads. Four tracking algorithms(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter, 2nd order Kalman filter, Singer-Kalman filter) have been compared and analyzed to evaluate the performance in test scenario.

• Journal of Navigation and Port Research
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• v.41 no.5
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• pp.297-302
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• 2017

The tracking filter plays a key role in accurate estimation and prediction of maneuvering the vessel's position and velocity. Different methods are used for tracking. However, the most commonly used method is the Kalman filter and its modifications. The ${\alpha}-{\beta}-{\gamma}$ filter is one of the special cases of the general solution provided by the Kalman filter. It is a third order filter that computes the smoothed estimates of position, velocity, and acceleration for the nth observation, and predicts the next position and velocity. Although found to track a maneuvering target with good accuracy than the constant velocity ${\alpha}-{\beta}$ filter, the ${\alpha}-{\beta}-{\gamma}$ filter does not perform impressively under high maneuvers, such as when the target is undergoing changing accelerations. This study aims to track a highly maneuvering target experiencing jerky motions due to changing accelerations. The ${\alpha}-{\beta}-{\gamma}$ filter is extended to include the fourth state that is, constant jerk to correct the sudden change of acceleration to improve the filter's performance. Results obtained from simulations of the input model of the target dynamics under consideration indicate an improvement in performance of the jerky model, ${\alpha}-{\beta}-{\gamma}-{\eta}$ algorithm as compared to the constant acceleration model, ${\alpha}-{\beta}-{\gamma}$ in terms of error reduction and stability of the filter during target maneuver.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1256-1260
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• 2006

This paper presents an enhanced ${\alpha}-{\beta}$ tracking filter whose ${\alpha}-{\beta}$ gains are updated by an approximation method at every scan to account for the transition of measurement dependent observation error variance in two-dimensional Cartesian coordinates. The approximate ${\alpha}-{\beta}$ gains are calculated from the amount of the change in the tracking index and the partial derivatives of the ${\alpha}-{\beta}$ gains with respect to a nominal tracking index. It is shown via simulation that the proposed tracker provides improved performance compared to the conventional ${\alpha}-{\beta}$ tracking filter.

• The Mathematical Education
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• v.10 no.1
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• pp.6-8
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• 1972

Metacompact인 부분 집합들을 5가지 형으로 분류하여 $\beta$-metacompact인 집합과 그 집합에 관하여 폐집합인 집합들과의 관계를 규명하였고, 위상공간의 임의의 부분집합의 Point finite filter를 정의하여 이 filter가 limit point를 갖는 조건과 이 집합이 $\beta$-metacompact인 조건과를 비교하였다. 다음에 5가지형의 metacompact들 사이의 관계를 조사하여 다음과 같은 관계를 얻었다. (1) $\beta$-metacompact ($\beta$-countably metacompact)인 부분집합의 내점들의 폐 부분집합은 $\alpha$-metacompact ($\alpha$-countably metacompact) 이다. (2) $\sigma$-metacompact of 이고, $\alpha$-countably metacompact인 부분집합은 $\alpha$-metacompact이다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.35-43
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• 2014

In this paper, we propose a performance enhancement method of the target tracking system for communication signal using the monopulse and the ${\alpha}{\beta}$ filter to keep the connection of the communication system between the airplane and the ground. We suggest the minimum distance measurement method for tracking error angle of the monopulse signal instead of the generally used method of MR(Monopulse Ratio) curve, and the ${\alpha}{\beta}$ filter with variable gain for enhancement of the tracking accuracy and the probability of re-tracking the monopulse signal under the disconnection of link. We show the performance enhancement of the proposed method of monopulse system using the measured MR Curve results of the prototype system. And also, the comparison of simulation results between the ${\alpha}{\beta}$ filter with variable gain and the ${\alpha}{\beta}$ filter with fixed gain shows the performance enhancement of the proposed ${\alpha}{\beta}$ filter. Using the proposed methods, we expect the enhanced performance of the existing target tracking system for communication signal only by changing the algorithm without hardware changes.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.302-304
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• 2016

The purpose of this paper is to draw comparison between the performance of ${\alpha}-{\beta}-{\gamma}$ filter and Kalman filter of a tracking module for ARPA system on board high dynamic warship. The comparison is based on the filters' capability to reduce residual error and maintain a stable transient response. The residual error is computed from the difference between the observed the predicted positions for the entire tracking period. The results indicate that the Kalman filter has a higher tracking accuracy compared to the optimal ${\alpha}-{\beta}-{\gamma}$ filter. However, both filters have a similar transient response.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.145-151
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• 2007

This paper considers new filter gains for the ${\alpha}\;-\;{\beta}$ tracker which is optimized particularly to minimize the tracking gate size. Optimizing the performance index which is composed of tracking errors due to target maneuver and measurement noise is not different from the existing method to obtain the ${\alpha}\;-\;{\beta}$ gains. However, holding the probability 0.997 that a target exists in the tracking gate and minimizing the gate size produce the new result not similar to the existing ${\alpha}\;-\;{\beta}$ gains.