• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.562-565
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• 1987

This paper addresses the development of the estimation algorithm to acquire target position, velocity and course using bearing-only measurements in two dimensional environment. System state equations are derived from modified polar coordinates instead of existing Cartesian coordinates system. The Extended Kalman Filter is used to constitute the estimation algorithm because of state equation's nonlinearity. The computer simulation is done to verify the performance of derived algorithm. Simulation result showed that estimated state value of filter was converged to the true value in 10 minutes.

• 전자공학회논문지
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• 제52권9호
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• pp.125-131
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• 2015

함정용 전자광학추적장비의 종속추적 표적지향 성능 개선을 위해 칼만 필터를 제안하였다. 전자광학추적장비의 추적기능 수행 시 표적지향 성능 저하의 주요 요인인 전송 지연 및 측정 오차를 칼만 필터를 활용할 경우 최소화할 수 있는 장점이 있다. 칼만 필터를 활용하여 방위각, 고각 방향으로의 표적지향 오차가 감쇄됨을 확인하고 전자광학추적장비에 적용해봄으로써 전자광학장비에서 빈번하게 발생하는 표적 추적 오차 개선 시스템으로 적합성을 제시하였다.

• 한국군사과학기술학회지
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• 제10권4호
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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.

• 한국군사과학기술학회지
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• 제2권1호
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• pp.170-182
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• 1999

본 논문에서는 전자광학 추적 시스템의 영상추적부가 가지는 시간지연 특성을 보상하여 추적 성능을 향상할 수 있는 방법을 제시하였다. 제안된 방법은 Smith 예측기와 칼만 필터를 사용하여 시선의 시간지연 현상 및 표적의 기동정보 지연에 대한 보상을 가능하게하여 기존의 PI 또는 Smith 예측기만의 제어루프를 사용한 경우보다 추적 오차를 매우 줄일 수 있다. 제안된 방법의 타당성 확인을 위하여 실제 EOTS에 적용하여 다양한 모의실험 및 실험을 실시하여 그 성능 향상을 확인하였다.

• 대한전기학회논문지
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• 제36권12호
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• pp.908-917
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• 1987

Most of moving targets are modelled as nonlinear dynamic equations. In recent years, the extended Kalman filter is frequently used for estimating their behaviors. The conditional Gaussian filter is more suitable than extended kalman filter in the filtering problem of nonlinear systems. But extended Kalman filter and conditional Gaussian filter often do not give optimal estimates and fail to track target trajectories because of its properties. Therefore it is desirable to use adaptive techniques to adapt target maneuvers. In this paper, we will discuss adaptive filtering technique using innovation process based on extended Kalman filter in real time, and suggest another maneuver estimation method using MRAS technique.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.905-908
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• 1988

In recent, Kalman filter technique has been much used as one of technique for tracking of the moving target. But some problem are still remained to be resolved. For example, when Kalman filter technique is applied to nonlinear system, the technique is nonoptimal estimator. Therefore, extended Kalman filter is proposed to reduce modeling error for nonlinear system. In this study, an extended Kalman filter in Cartesian coordinates is described for moving target, when the radar sensor measures range, azimuth and elevation angle in polar coordinates. And an approximate gain computation algorithm is proposed. In this approach, Kalman gains are computed for three uncoupled filter and multiplied by a Jacobian transformation determined from the measured target position and orientation.

• 로봇학회논문지
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• 제16권4호
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• pp.336-344
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• 2021

This paper presents a robust deep learning-based human tracking framework in crowded environments. For practical human tracking applications, a target must be robustly tracked even in undetected or overcrowded situations. The proposed framework consists of two parts: robust deep learning-based human detection and tracking while recognizing the aforementioned situations. In the former part, target candidates are detected using Detectron2, which is one of the powerful deep learning tools, and their weights are computed and assigned. Subsequently, a candidate with the highest weight is extracted and is utilized to track the target human using a Kalman filter. If the bounding boxes of the extracted candidate and another candidate are overlapped, it is regarded as a crowded situation. In this situation, the center information of the extracted candidate is compensated using the state estimated prior to the crowded situation. When candidates are not detected from Detectron2, it means that the target is completely occluded and the next state of the target is estimated using the Kalman prediction step only. In two experiments, people wearing the same color clothes and having a similar height roam around the given place by overlapping one another. The average error of the proposed framework was measured and compared with one of the conventional approaches. In the error result, the proposed framework showed its robustness in the crowded environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권5호
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• pp.1166-1179
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• 2013

In wireless sensor network (WSN) environments, environmental noises are generated by, for example, small passing animals, crickets chirping or foliage blowing and will interfere target detection if the noises are higher than the sensor threshold value. For accurate tracking by acoustic WSNs, these environmental noises should be filtered out before initiating track. This paper presents the effect of environmental noises on target tracking and proposes a new algorithm for the noise mitigation in acoustic WSNs. We find that our noise mitigation algorithm works well even for targets with sensing range shorter than the sensor separation as well as with longer sensing ranges. It is also found that noise duration at each sensor affects the performance of the algorithm. A detection algorithm is also presented to account for the Doppler effect which is an important consideration for tracking higher-speed ground targets. For tracking, we use the weighted sensor position centroid to represent the target position measurement and use the Kalman filter (KF) for tracking.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.552-554
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• 1998

In target tracking problems the fixed gain Kalman filter is primarily used to predict a target state vector. This filter, however, has a poor precision for maneuvering targets while it has a good performance for non-maneuvering targets. To overcome the problem this paper proposes the system which estimates the acceleration with neural networks using the input estimation technique. The ability to efficiently fuse information of different forms is one of the major capabilities of trained multi-layer neural networks. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features can be utilized as inputs for estimating target maneuvers. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates. The features used as inputs can be extracted from the combinations of innovation data and heading changes, and for this we set the two dimensional model. The properly trained neural network system outputs the acceleration estimates and compensates for the primary Kalman filter. Finally the proposed system shows the optimum performance.

• 대한전자공학회논문지SP
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• 제38권5호
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• pp.583-597
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• 2001

Tracking the targets of interest has been one of the major research areas in radar surveillance system. We formulate the tracking problem as an incomplete data problem and apply the EM algorithm to obtain the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The difference is that the measurement-update deals with multiple measurements and the parameter-update can estimate the system parameters. Through extensive experiments, it turns out that the proposed system is better than PDAF and NNF in tracking the targets. Also, the performance degrades gracefully as the disturbances become stronger.