• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.2
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• pp.118-128
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• 2012

The goal of this paper is to design the target tracking controller for a quadrotor micro UAV using a vision sensor. First of all, the mathematical model of the quadrotor was estimated through the Prediction Error Method(PEM) using experimental input/output flight data, and then the estimated model was validated via the comparison with new experimental flight data. Next, the target tracking controller was designed using LQR(Linear Quadratic Regulator) method based on the estimated model. The relative distance between an object and the quadrotor was obtained by a vision sensor, and the altitude was obtained by a ultra sonic sensor. Finally, the performance of the designed target tracking controller was evaluated through flight tests.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.172-181
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• 2019

This paper deals with the problem of generating the energy optimal trajectory which is intended to enhance the target tracking performance of a passive homing missile. Noticing that the essence of passive target tracking is the range estimation problem, the target information gathered by passive measurements can be readily analyzed by introducing the range estimator designed in line-of-sight(LOS) frame. Moreover, for the linear filter structure of the suggested range estimator, the cost function associated with the target information is clearly expressed as a function of the line-of-sight rate. Based on this idea, the optimal missile trajectory maximizing the target information is obtained by solving the saddle point problem for an indefinite quadratic cost which consists of the target information and the energy. It is shown that, different from the previous heuristic approaches, the guidance command producing the optimal passive homing trajectory is produced by the modified proportional navigation guidance law whose navigation constant is determined by the weighting coefficient for target information cost.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2340-2344
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• 2003

This paper considers a tracking filter algorithm which can track a maneuvering target. Multiresolutional Interacting Multiple Model (MRIMM) algorithm is proposed to reduce computational burden. In this paper multiresolutional state space model equation and multiresolutional measurement equation are derived by using wavelet transform. This paper shows the outline of MRIMM algorithm. Simulation results show that MRIMM algorithm maintains a good tracking performance and reduces computational burden.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.783-789
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• 2011

Target tracking performance improvement using multi-sensor data fusion is a challenging work. However, biases in the measurements should be removed before various data fusion techniques are applied. In this paper, a bias removing algorithm using measurement data from multi-radar tracking systems is proposed and evaluated by computer simulation. To predict bias estimation performance in various geometric relations between the radar systems and target, a system observability index is proposed and tested via computer simulation results. It is also studied that target tracking which utilizes multi-sensor data fusion with bias-removed measurements results in better performance.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.218-223
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• 1993

In this paper, we modify a multiple target angle tracking algorithm presented by Sword et al.. The predicted estimates, instead of the existing estimates, of the target angles are updated by the most recent output of the sensor array to improve the tracking performance of the algorithm for crossing targets. Also, the least square solution is modified to avoid abnormally large angle innovations when the target angles are very close. The improved performance of the proposed algorithm is demonstrated by computer simulations.

• ETRI Journal
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• v.38 no.5
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• pp.1019-1029
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• 2016

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.51-60
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• 2007

This paper describes a visual tracking control law of an Unmanned Aerial Vehicle(UAV) for monitoring of structures and maintenance of bridges. It presents a control law based on computer vision for quasi-stationary flights above a planar target. The first part of the UAV's mission is the navigation from an initial position to a final position to define a desired trajectory in an unknown 3D environment. The proposed method uses the homography matrix computed from the visual information and derives, using backstepping techniques, an adaptive nonlinear tracking control law allowing the effective tracking and depth estimation. The depth represents the desired distance separating the camera from the target.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.335-337
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• 1994

The correlation tracker developed by John M. Fitts in 1979 is the most complex to mechanize but provides the best tracking performance in a low SNR condition. Correlation tracker would rewove the requirements for optimizing threshold and has no need to know information about the target. But if the displacement of the target is large, the tracking error of the correlation tracker tends to diverge. In this paper, we suggest a precision image tracking algorithm which improves the tracking performance via iterative application of the matched filter estimation algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1067-1071
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• 2013

A 3D visual sensing method using a laser structured beam is presented for robotic tracking applications in a simple and reliable manner. A cylindrical shaped laser structured beam is proposed to measure the pose and position of the target surface. When the proposed laser beam intersects on the surface along the target trajectory, an elliptic pattern is generated. Its ellipse parameters can be induced mathematically by the geometrical relationship of the sensor coordinate and target coordinate. The depth and orientation of the target surface are directly determined by the ellipse parameters. In particular, two discontinuous points on the ellipse pattern, induced by seam trajectory, indicate mathematically the 3D direction for robotic tracking. To investigate the performance of this method, experiments with a 6 axis robot system are conducted on two different types of seam trajectories. The results show that this method is very suitable for robot seam tracking applications due to its excellence in accuracy and efficiency.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.836-842
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• 2015

In this paper, the target size and model based target size estimator (TMBE) algorithm is presented for iimaging infrared (IIR) seeker. At the imaging seeker, target size information is important factor for accurate tracking. The model based target size estimator filter (MBEF) algorithm was proposed to estimate target size at imaging infrared seeker. But, the model based target size estimator filter algorithm need to know relative distance from the target. In order to overcome the problem, we propose target size and model based target size estimator filter (TMBEF) algorithm which based on the target size. The performance of proposed algorithm is tested at target intercept scenario. The experiment results show that the proposed algorithm has the accurate target size estimating performance.