• Journal of KIISE:Computer Systems and Theory
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• v.36 no.4
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• pp.274-282
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• 2009

Various location tracking sensors; such as GPS, INS, radar, and optical equipment; are used for tracking moving targets. In order to effectively track moving targets, it is necessary to develop an effective fusion method for these heterogeneous devices. There have been studies in which the estimated values of each sensors were regarded as different models and fused together, considering the different error characteristics of the sensors for the improvement of tracking performance using heterogeneous multi-sensor. However, the rate of errors for the estimated values of other sensors has increased, in that there has been a sharp increase in sensor errors and the attempts to change the estimated sensor values for the Sensor Probability could not be applied in real time. In this study, the Sensor Probability is obtained by comparing the RMSE (Root Mean Square Error) for the difference between the updated and measured values of the Kalman filter for each sensor. The process of substituting the new combined values for the Kalman filter input values for each sensor is excluded. There are improvements in both the real-time application of estimated sensor values, and the tracking performance for the areas in which the sensor performance has rapidly decreased. The proposed algorithm adds the error characteristic of each sensor as a conditional probability value, and ensures greater accuracy by performing the track fusion with the sensors with the most reliable performance. The trajectory of a UAV is generated in an experiment and a performance analysis is conducted with other fusion algorithms.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.325-330
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• 2022

The sensors of aircraft are necessity for mission performance and fusion process of data from them is applied for increase of mission efficiency and decrease of aircraft pilot workload. Data fusion is applied and developed to provide pilot a series of more processed data format about a specific target from sensors in aircraft. Military aircraft currently in operation are linked with a tactical data link such as Link-16 to display improved tactical situation to pilots to increase mission efficiency. By fusing the sensor data with improved accuracy obtained as the sensors' performance mounted on the aircraft become higher and the tactical situation information received through the tactical data link, it provides the pilot with a highly reliable tactical situation and mission environment, and expects efficient mission performance and high survivability. In this paper, a fusion architecture to produce fused data with realtime information from the sensors and data through a tactical data link is shown.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.657-659
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• 1998

In this paper we discuss an algorithm to discriminate a target under track against multiple acoustic counter-measure (ACM) sources, based on sequential testings of multiple hypotheses. The ACM sources are separated from the target under track and generate, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target tracking and to help the true target evade a pursuer. The proposed algorithm uses as a test statistic a function of both the sequences of processed waveform signature and the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting positions of the ACM sources. Numerical experiments on various scenarios show that the proposed algorithm discriminates the target faster with a higher probability of success than the algorithm using only the innovation sequences from extended Kalman filters.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.262-269
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• 2010

This paper presents a probabilistic head tracking method, mainly applicable to face recognition and human robot interaction, which can robustly track human head against various variations such as pose/scale change, illumination change, and background clutters. Compared to conventional particle filter based approaches, the proposed method can effectively track a human head by regularizing the sample space and sequentially weighting multiple visual cues, in the prediction and observation stages, respectively. Experimental results show the robustness of the proposed method, and it is worthy to be mentioned that some proposed probabilistic framework could be easily applied to other object tracking problems.

• Advances in aircraft and spacecraft science
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• v.8 no.1
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• pp.31-51
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• 2021

Selective laser melting (SLM), one of the most widely used powder bed fusion (PBF) additive manufacturing (AM) technology, enables the fabrication of customized metallic parts with complex geometry by layer-by-layer fashion. However, SLM inherently poses several problems such as the discontinuities in the molten track and the steep temperature gradient resulting in a high degree of residual stress. To avoid such defects, thisstudy proposes a temperature thread multiscale model of SLM for the evaluation of the process at different scales. In microscale melt pool analysis, the laser beam parameters were evaluated based on the predicted melt pool morphology to check for lack-of-fusion or keyhole defects. The analysis results at microscale were then used to build an equivalent body heat flux model to obtain the residual stress distribution and the part distortions at the macroscale (part level). To identify the source of uneven heat dissipation, a liquid lifetime contour at macroscale was investigated. The predicted distortion was also experimentally validated showing a good agreement with the experimental measurement.

• Journal of Powder Materials
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• v.31 no.4
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• pp.308-317
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• 2024

Maraging steel has excellent mechanical properties resulting from the formation of precipitates within the matrix through aging treatment. Maraging steel fabricated by the laser powder bed fusion (LPBF) process is suitable for applications including precise components and optimized design. The anisotropic characteristic, which depends on the stacking direction, affects the mechanical properties. This study aimed to analyze the influence of anisotropy on the wear behavior of maraging steel after aging treatment. The features of additive manufacturing tended to disappear after heat treatment. However, some residual cellular and dendrite structures were observed. In the wear tests, a high wear rate was observed on the building direction plane for all counter materials. This is believed to be because the oxides formed on the wear track positively affected the wear characteristics; meanwhile, the bead shape in the stacking direction surface was vulnerable to wear, leading to significant wear.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.313-322
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• 2014

This paper is to survey and put in perspective the working methods of multi-target tracking in clutter. This paper includes theories and practices for data association and related filter structures and is motivated by increasing interest in the area of target tracking, security, surveillance, and multi-sensor data fusion. It is hoped that it will be useful in view of taking into consideration a full understanding of existing techniques before using them in practice.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.243-248
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• 2013

In wireless sensor networks, consensus algorithms for dynamic systems may flexibly usable for their data fusion of a sensor network. In this paper, a distributed data fusion filter is implemented using an average consensus based on distributed sensor data, which is composed of some sensor nodes and a sink node to track the mean values of n sensors' data. The consensus filter resolve the problem of data fusion by a distribution Kalman filtering scheme. We showed that the consensus filter has an optimal convergence to decrease of noise propagation and fast tracking ability for input signals. In order to verify for the results of consensus filtering, we showed the output signals of sensor nodes and their filtering results, and then showed the result of the combined signal and the consensus filtering using zeegbee communication.

• Journal of Powder Materials
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• v.29 no.5
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• pp.390-398
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• 2022

Recently, considerable attention has been given to nickel-based superalloys used in additive manufacturing. However, additive manufacturing is limited by a slow build rate in obtaining optimal densities. In this study, optimal volumetric energy density (VED) was calculated using optimal process parameters of IN718 provided by additive manufacturing of laser powder-bed fusion. The laser power and scan speed were controlled using the same ratio to maintain the optimal VED and achieve a fast build rate. Cube samples were manufactured using seven process parameters, including an optimal process parameter. Analysis was conducted based on changes in density and melt-pool morphology. At a low laser power and scan speed, the energy applied to the powder bed was proportional to ${\frac{P}{\sqrt{V}}}$ and not ${\frac{P}{V}}$. At a high laser power and scan speed, a curved track was formed due to Plateau-Rayleigh instability. However, a wide melt-pool shape and continuous track were formed, which did not significantly affect the density. We were able to verify the validity of the VED formula and succeeded in achieving a 75% higher build rate than that of the optimal parameter, with a slight decrease in density and hardness.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.773-777
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• 2016

In this paper, a novel multi-object tracking method to track an unknown number of objects is proposed. To handle multiple object states and uncertain observations efficiently, a probability hypothesis density (PHD) filter is adopted and modified. The PHD filter is capable of reducing false positives, managing object appearances and disappearances, and estimating the multiple object trajectories in a unified framework. Although the PHD filter is robust in cluttered environments, it is vulnerable to false negatives. For this reason, we propose to exploit local observations in an RFS of the observation model. Each local observation is generated by using an online trained object detector. The main purpose of the local observation is to deal with false negatives in the PHD filtering procedure. The experimental results demonstrated that the proposed method robustly tracked multiple objects under practical situations.