• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.469-474
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• 1990

The implementation of modern guidance law derived from optimal control theory requires accurate current states of target, for example, position, velocity and acceleration etc. But there is no sensors that measure the target states directly. So they are estimated from measurable data. For atmospheric missile engagement, direct application of the modern guidance laws may result In deterioration of Intercept performance because of poor observability associated with angles only-measurements by passive seeker and homing geometry. In this paper, a trajectory modulation method called "adaptive Intermittent maneuvers" is added to the modern guidance law, so the observability is enhanced and, consequently, improved the intercept performance. The estimation algorithm called "modified gain pseudo-measurement filter" is used for tracking filter. It is assumed that the passive seeker measure the angles between line of sight and Inertial frame. The Monte-Carlo simulation for realistic air-to-air Intercept scenario are conducted to demonstrate the effectiveness of intermittent maneuvers.ermittent maneuvers.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_1
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• pp.125-137
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• 2020

Tracking micro-sized insects is one of the challenges of protecting ecosystems and biodiversity. In this study, we propose an approach for the autonomous tracking of micro-sized flying insects, and develop an unmanned aerial vehicle (UAV)-based robotic system. The Kalman filter is applied to the received signal strength emitted from radio telemetry to estimate the position while reducing the measurement error and noise. The autonomous tracking strategy is a method in which the UAV rotates at one point to measure the signal strength and control its position in the strongest direction of the signal. We also design a system architecture comprising a tracking sensor system and a UAV system for micro-sized insects. The estimation and autonomous tracking of the target position by the proposed system are verified and evaluated through dynamic simulation. Therefore, in this study, we propose and validate a UAV-based tracking system for micro-sized flying insects, which has not been proposed in studies conducted thus far.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.3
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• pp.233-241
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• 2014

This paper contributes to development of a new method for detecting rear-side vehicles and estimating the positions for blind spot region or providing the lane change information by using vision systems. Because the real image acquired during car driving has a lot of information including the target vehicle and background image as well as the noises such as lighting and shading, it is hard to extract only the target vehicle against the background image with satisfied robustness. In this paper, the target vehicle has been detected by repetitive image processing such as sobel and morphological operations and a Kalman filter has been also designed to cancel the background image and prevent the misreading of the target image. The proposed method can get faster image processing and more robustness rather than the previous researches. Various experiments were performed on the highway driving situations to evaluate the performance of the proposed algorithm.

• Journal of Satellite, Information and Communications
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• v.12 no.1
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• pp.64-71
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• 2017

In this paper, Kalman filter and RRWA algorithm are used to estimate the accurate target in IR-UWB (Impulse-Radio Ultra Wideband) radar system, which enables accurate location recognition of indoors and outdoors with low cost and low power consumption. In the signal reflected by the target, unnecessary signals exist in addition to the target signal. We have tried to remove unnecessary signals and to derive accurate target signals and improve performance. The location of the targets is estimated in real time with one transmitting antenna and one receiving antenna. The Kalman filter was used to remove the background noise and the RRWA algorithm was used to remove the reflected signal. In this paper, we think that it will be useful to study the accurate distance estimation and tracking in future target estimation.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.237-240
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• 2000

CDHA환경에서 단말기 위치 결정은 여러 부가적인 서비스 응용에 대한 필요성 때문에 활발히 연구가 진행되고 있다. 그러나 기존의 위치 결정 알고리즘은 현재의 정보만을 활용했기 때문에 위치 오차에 대한 성능 향상에 한계점을 드러내고 있다. 따라서 이전 시간의 단말기 위치 정보가 포함된 Kalman Filter를 사용한다면 위치 에러에 대해 향상된 성능을 보일 것이다 그렇지만 실제 단말기 사용자의 움직임은 Meneuvering Target에 가깝기 때문에 단순히 Kalman Filter를 이용한 위치 오차 성능 개선보다는, 여러 개의 Kalman Filter Model들을 응용하는 IMM을 이용하는 경우에 보다 나은 결과가 도출될 것이다. 실제로 단말기 위치 오차에 대한 Kalman Filter와 IMM을 적용한 경우의 비교 분석 결과, IMM을 적용한 경우가 위치 에러를 최소화 할 수 있었다.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.711-715
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• 2001

In this paper, an effective filter structure for filtering of target glint in tracking radar systems is used to improve the performance of CLOS(Command to Line-Of-Sight) guidance. The filter decouples range and angel channels to that it has a sound mathematical basis as well as computation efficiency as applied to the IMM algorithm. The effective filter structure in conjunction with CLOS guidance is tested by a series of simulation runs and it is shown to have superior performance compared with the other filter structures.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1098-1101
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• 2009

최근 센서 기술이 발전함에 따라 센서들의 유비쿼터스 환경에서의 활용방법에 대한 연구가 진행되고 있다. 현재 존재하는 센서들 중 Zigbee 센서는 저전력, 초소형 등의 특징을 가지고 센서들이 통신을 하는 센서로써 유지비용과 이동성에 있어서 다른 센서들보다 성능이 우위에 있다. Zigbee 센서는 신호를 Broadcasting하여 다른 Zigbee 센서와 통신을 하게 된다. 이때 이 신호의 세기를 나타내는 RSS와 Triangulation을 통하여 위치를 파악할 수 있다. 그리고 이 결과를 Particle Filter 알고리즘을 통하여 위치추정의 정확도를 높일 수 있다. 또한 유비쿼터스 환경에서의 활용 가능성 파악을 위하여 실제 집 환경의 Testbed를 구축하여 시뮬레이션을 진행하였다. 멀티 타겟의 위치 추정을 위하여 Zigbee 센서의 Time Cycle 조정을 통하여 Particle Filter 알고리즘을 사용하여 위치 추정 오차를 시뮬레이션으로 성능평가를 하였고 결과를 통하여 멀티 타겟의 경로를 분석하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.740-745
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• 2003

Motion tracking and object segmentation are the most fundamental and critical problems in vision tasks suck as motion analysis. An active contour model, snake, was developed as a useful segmenting and tracking tool for rigid or non-rigid objects. Snake is designed no the basis of snake energies. Segmenting and tracking can be executed successfully by energy minimization. In this research, two new paradigms for segmentation and tracking are suggested. First, because the conventional method uses only intensity information, it is difficult to separate an object from its complex background. Therefore, a new energy and design schemes should be proposed for the better segmentation of objects. Second, conventional snake can be applied in situations where the change between images is small. If a fast moving object exists in successive images, conventional snake will not operate well because the moving object may have large differences in its position or shape, between successive images. Snakes's nodes may also fall into the local minima in their motion to the new positions of the target object in the succeeding image. For robust tracking, the condensation algorithm was adopted to control the parameters of the proposed snake model called "adaptive color snake model(SCSM)". The effectiveness of the ACSM is verified by appropriate simulations and experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.539-544
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• 2004

We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.

• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.167-177
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• 2019

Even though so much progresses have been achieved in Multiple Object Tracking (MOT), most of reported MOT methods are not still satisfactory for commercial embedded products like Pan-Tilt-Zoom (PTZ) camera. In this paper, we propose a real-time multiple pedestrians tracking method for embedded environments. First, we design a new light weight convolutional neural network(CNN)-based pedestrian detector, which is constructed to detect even small size pedestrians, as well. For further saving of processing time, the designed detector is applied for every other frame, and Kalman filter is employed to predict pedestrians' positions in frames where the designed CNN-based detector is not applied. The pose orientation information is incorporated to enhance object association for tracking pedestrians without further computational cost. Through experiments on Nvidia's embedded computing board, Jetson TX2, it is verified that the designed pedestrian detector detects even small size pedestrians fast and well, compared to many state-of-the-art detectors, and that the proposed tracking method can track pedestrians in real-time and show accuracy performance comparably to performances of many state-of-the-art tracking methods, which do not target for operation in embedded systems.