• 한국정밀공학회지
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• 제15권7호
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• pp.175-181
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• 1998

This paper describes realtime visual tracking system of moving object for three dimensional target using EKF(Extended Kalman Filter). We present a new realtime visual tracking using EKF algorithm and image prediction algorithm. We demonstrate the performance of these tracking algorithm through real experiment. The experimental results show the effectiveness of the EKF algorithm and image prediction algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.125-130
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• 1991

This paper presents a tracking filter using pseudomeasurements in an estimated line-of-sight Cartesian coordinate system(ELCCS) whose x-axis is on the line-of-sight to an estimated target position. A target dynamics model and a measurement equation in the ELCCS are derived first and then a tracking filter in the ELCCS named moving coordinate tracking filter(MCTF) is proposed. It is shown that this MCTF is equivalent to a Kalman filter in the inertial Cartesian coordinate system which is widely used in the target tracking system. By approximating the MCTF for a pseudomeasurement noise and an error covariance matrix in the ELCCS, decoupling of three axes can be achieved. In this case, named decoupled moving coordinate tracking filter(DMCTF), computation time can be drastically reduced by utilizing its parallel structure. Finally, the stochastic properties of the MCTF and DMCTF are presented. Especially, a sufficient condition of nondestabilizing deviation for the DMCTF is proposed. The performance of the MCTF and DMCTF are compared with a conventional Kalman tracking filter.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.108-113
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• 1991

In this paper, an improved method for tracking maneuvering target is proposed. The proposed tracking filter is constructed by combining the input estimation approach with the variable dimension filtering approach. In this approach, the filter also provides the estimated time instant at which target starts maneuver, when the target maneuver is detected. Using this estimated maneuvering time, the maneuver input is estimated and the tracking system changes to the maneuver model. Simulations are performed to demonstrate the efficiency of the proposed tracking filter.

• 대한임베디드공학회논문지
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• 제6권1호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
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• pp.126-130
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• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion (FBFE) based on virus evolutionary genetic algorithm(VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter (EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FBFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by identifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• 한국멀티미디어학회논문지
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• 제19권5호
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• pp.890-900
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• 2016

This paper presents a multiple target tracking system using target feature information. In the proposed system, the state of target is defined as its kinematic as well as feature : the kinematic includes a location and a velocity; the feature contains the image correlation between a prior target and a current measurement. The feature information is used for generating the validation matrix and association probability of joint probabilistic data association (JPDA) algorithm. Through the Kalman filter, the target kinematic is updated. Then the tracking information is cycled by the track management algorithm. The system has been evaluated using the images obtained from Electro-Optics/ InfraRed (EO/IR) sensor. It is verified that the proposed system can reduce the complexity burden of JPDA process and can enhance the track maintenance rate.

• 전자공학회논문지S
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• 제36S권7호
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• pp.43-49
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• 1999

다중표적 추적시스템은 여러 개의 표적물을 동시에 추적한다. 이와 같은 시스템에서는 여러 개의 표적물들에 관한 위치정보들과 추적중인 표적물들과의 정보융합과정이 요구된다. 본 논문에서는 이러한 경우에 추적중인 표적물들이 지니는 예측위치들의 신뢰구간을 이용하여 측정한 위치정보들을 각각의 표적물들에 할당하는 방법을 제안하였다. 제안된 방법을 실제의 교통정보에 적용하여 그 우수한 특성을 살펴보았다.

• Journal of Mechanical Science and Technology
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• 제19권9호
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• pp.1742-1752
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• 2005

This paper presents a Multiple Target Tracking (MTT) Adaptive Cruise Control (ACC) system which consists of three parts; a multi-model-based multi-target state estimator, a primary vehicular target determination algorithm, and a single-target adaptive cruise control algorithm. Three motion models, which are validated using simulated and experimental data, are adopted to distinguish large lateral motions from longitudinally excited motions. The improvement in the state estimation performance when using three models is verified in target tracking simulations. However, the performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. The MTT-ACC system is tested under lane changing situations to examine how much the system performance is improved when multiple models are incorporated. Simulation results show system response that is more realistic and reflective of actual human driving behavior.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1500-1504
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• 2005

This paper proposes the design methodology of genetic algorithm (GA)-based fuzzy Kalman filter for tracking the maneuvering target. The performance of the standard Kalman Filter (SKF) has been degraded because mismatches between the modeled target dynamics and the actual target dynamics. To solve this problem, we use the method to estimate the increment of acceleration by a fuzzy system using the relation between maneuver filter residual and non-maneuvering one. To optimize the fuzzy system, a genetic algorithm (GA) is utilized and this is then tuned by the fuzzy logic correction. Finally, the tracking performance of the proposed method has been compared with those of the input estimation (IE) technique and the intelligent input estimation (IIE) through computer simulations.

• 대한전기학회논문지
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• 제41권4호
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• pp.411-421
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• 1992

In the conventional track while scan (TWS) system, there are two major functions to be performed : detection and tracking. These two functions are normally designed and optimised independently. So TWS algorithm ignores the available decision features that can help in resolving the plot-to-track association ambiguity. Therefore conventional TWS system cna't track the targets in a densed multi-target environment. This paper presents a new TWS algorithm for multi-target track to solve the existing TWS system problem in clutter environment. The algorithm proposed in this paper is derived by modifying the part of joint probabilistic data association (JPDA) algotithm to get the one to one correspondence instead of multiple correspondence and combined with maneuvering detection logic so that it could also track the low maneuvering targets. Simulations to confirm the performance are done in crossing, parallel and maneuvering target. The proposed algorithm was successfully tracking targets above target situations.