• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.99-99
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• 2000

An effect ive filter algorithm that can manage radar beam pointing efficiently is needed to track multi-target in the air. For effective beam management the filter has lobe good enough to predict future position of target and based on this filter output radar beam is control led to point toward the predicted target position in the air. In this paper, we investigate the ${\alpha}$-${\beta}$ filter known for its brief filter structure with the steady-state Kalman filter gain, the ruv filter, and the coordinate-transformed filter that can decouple the measurement noise variance.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.696-701
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• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1711-1725
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• 2014

Multi-target tracking is the main purpose of many video surveillance applications. Recently, multi-target tracking based on the particle filter method has achieved robust results by using the data association process. However, this method requires many calculations and it is inadequate for real time applications, because the number of associations exponentially increases with the number of measurements and targets. In this paper, to reduce the computational cost of the data association process, we propose a novel multi-target tracking method that excludes particle samples in the overlapped predictive region between the target to track and marginal targets. Moreover, to resolve the occlusion problem, we define an occlusion mode with the normal dynamic mode. When the targets are occluded, the mode is switched to the occlusion mode and the samples are propagated by Gaussian noise without the sampling process of the particle filter. Experimental results demonstrate the robustness of the proposed multi-target tracking method even in occlusion.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.7-13
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• 2015

Unmanned air vehicles (UAVs) are getting popular not only as a private usage for the aerial photograph but military usage for the surveillance, reconnaissance and supply missions. For an UAV to successfully achieve these kind of missions, geolocation (localization) must be implied to track an interested target or fly by reference. In this research, we adopted multi-sensor fusion (MSF) algorithm to increase the accuracy of the geolocation and verified the algorithm using two multicopter UAVs. One UAV is equipped with an optical camera, and another UAV is equipped with an optical camera and a laser range finder. Throughout the experiment, we have obtained measurements about a fixed ground target and estimated the target position by a series of coordinate transformations and sequential Kalman filter. The result showed that the MSF has better performance in estimating target location than the case of using single sensor. Moreover, the experimental result implied that multi-sensor geolocation algorithm is able to have further improvements in localization accuracy and feasibility of other complicated applications such as moving target tracking and multiple target tracking.

• Journal of the Korea Society of Computer and Information
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• v.21 no.11
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• pp.31-38
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• 2016

In this paper, we propose multi-scale infrared target detection algorithm with varied filter size using integral image. Filter based target detection is widely used for small target detection, but it doesn't suit for large target detection depending on the filter size. When there are multi-scale targets on the sky background, detection filter with small filter size can not detect the whole shape of the large targe. In contrast, detection filter with large filter size doesn't suit for small target detection, but also it requires a large amount of processing time. The proposed algorithm integrates the filtering results of varied filter size for the detection of small and large targets. The proposed algorithm has good performance for both small and large target detection. Furthermore, the proposed algorithm requires a less processing time, since it use the integral image to make the mean images with different filter sizes for subtraction between the original image and the respective mean image. In addition, we propose the implementation of real-time embedded system using FPGA.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.3
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• pp.343-349
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• 2012

To detect targets for autonomous navigation of unmanned ground vehicle, mounted sensors are required to work all-weather condition. In this point of view, the FMCW radar is quietly appropriate. In this paper, we present development results of target signal simulator for multi-beam type FMCW radar. A target signal simulator make pseudo target signals which simulates multiple moving targets. And we describe how to make hit information for each target in multi-beam type radar. The developed methods are utilized for target tracking device. Moreover it can be applied to similar target signal simulator.

• Nuclear Engineering and Technology
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• v.53 no.9
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• pp.3026-3034
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• 2021

Multi-target X-ray tube is a new type X-ray source, and can be applied in many fields such as sensitive X-ray fluorescence analysis and medical imaging. In this work, we report an electric field modulation multi-target X-ray tube, which contains four targets (Cr, Ni, Au, Mo) coated on a Beryllium (Be) window. A four-valve electric field deflector was developed to deflect the electron beam to bombard the corresponding targets. Particle dynamics analysis software was employed to simulate the particle tracking of electron beam. The results show that the 30 keV electron beam could get a 6.7 mm displacement on the target plane by 10⁵ V/m electric field. The focus areas are about 2 mm × 5 mm and 4 mm × 2.5 mm after deflection in two directions. Thermal behavior calculated by ANSYS shows that the designed target assembly could withstand a 10 W continuous power. The optimum target thicknesses and emission spectra were obtained by Geant4 when the thickness of Be window was 300 mm and the electron beam incident angle was 0.141 rad. The results indicate that this multi-target X-ray tube could provide different X-ray sources effectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.139-144
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• 2005

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion. have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.209-218
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• 2013

Military surveillance systems with electro-optical sensors can be used to track a number of targets efficiently and reliably. In MTT (Multi-Target Tracking), joint events in which different tracks share the same measurements may occur. Measurement-to-track assignment are computationally challenging because of the number of operations increases exponentially with number of tracks and number of measurements. IHPDA (Integrated Highest Probability Data Association) based on a 2D-Assignment technique can find an optimal solution for measurement to track one-to-one assignments for complex environments. In this paper, LM-IHPDA (Linear Multi-Target IHPDA) which does not need to form all feasible joint events of association and thus the computational load is linear in the number of tracks and the number of measurements. Simulation studies illustrate the effectiveness of this approach in an infrared image environment.