• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.258-264
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• 2015

The first 77 GHz transceiver that applies a heterodyne structure-based linear frequency modulation-frequency shift keying (LFM-FSK) front-end module (FEM) is presented. An LFM-FSK waveform generator is proposed for the transceiver design to avoid ghost target detection in a multi-target environment. This FEM consists of three parts: a frequency synthesizer, a 77 GHz up/down converter, and a baseband block. The purpose of the FEM is to make an appropriate beat frequency, which will be the key to solving problems in the digital signal processor (DSP). This paper mainly focuses on the most challenging tasks, including generating and conveying the correct transmission waveform in the 77 GHz frequency band to the DSP. A synthesizer test confirmed that the developed module for the signal generator of the LFM-FSK can produce an adequate transmission signal. Additionally, a loop back test confirmed that the output frequency of this module works well. This development will contribute to future progress in integrating a radar module for multi-target detection. By using the LFM-FSK waveform method, this radar transceiver is expected to provide multi-target detection, in contrast to the existing method.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.4
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• pp.350-357
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• 2015

Many countries have been developing and operating an underwater surveillance system in order to protect their oceanic environment from infiltrating hostile marine forces which intend to lay mines, conduct reconnaissance and destroy friendly ships anchored at the harbor. One of the most efficient methods to detect unidentified submarine approaching harbor is sensing variation of magnetism of target by magnetic sensors. This measurement system has an advantage of high possibility of detection and low probability of false alarm, compared to acoustic sensors, although it has relatively decreased detection range. The contents of this paper mainly cover the analysis of possible effectiveness of magnetic sensors. First of all, environmental characteristics of surveillance area and magnetic information of simulated targets has been analyzed. Subsequently, a signal processing method of separating target from geomagnetic field and methods of estimating target location has been proposed.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.53-60
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• 2013

We propose a method for improving detection speed of small target detection system using SAD algorithm. First, the proposed method deletes clutters using a median filter. Next, it does closing and opening operation using various size of structure elements, and extracts candidate pixels for a target with subtraction operation between the results of closing and opening operation. It finally detects a small target using a gaussian distance function from the candidate pixels. To improve detection speed, it detects a target performing SAD algorithm only for the predicted target areas for next every 7 frames. The proposed method not only enables a real time process because it considers only predicted area but also shows detecting rate of 97%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.99-108
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• 2004

In this paper, a new fast detection and clutter rejection method is proposed for CCD-image-based Automatic Target Detection System. For defence application, fast computation is a critical point, thus we concentrated on the ability to detect various targets with simple computation. In training stage, 1D template set is generated by regional vertical projection and K-means clustering, and binary tree structure is adopted to reduce the number of template matching in test stage. We also use adaptive skip-width by Correlation-based Adaptive Predictive Search(CAPS) to further improve the detecting speed. In clutter rejection stage, we obtain Fourier Descriptor coefficients from boundary information, which are useful to rejected clutters.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.12-18
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• 2001

SAR radar and FLIR images, which are taken from sensors on aircrafts or satellites, are compressed prior to transmission to facilitate rapid transfer through the limited bandwidth channels. In this case, it is important that it achieves compression ratio as high as possible as well as high target detection rate. In this paper a joint ATR-compression system based on the subband coding and VQ is proposed, which utilizes the encoder as a predictor or classifier for target detection. Simulation result shows that the proposed system achieves a relatively high level of target detection performance as well as a high compression ratio over 200:1.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.407-417
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• 2019

When a free-falling ballistic missile intercepts a maritime target in a sea clutter environment at high grazing angle, detection performance of the ballistic missile's seeker can be rapidly degraded by the effect of sea clutter. To solve this problem, it is necessary to verify the performance of maritime target detection via simulations based on various scenarios. We accomplish this by applying a two-dimensional cell -averaging constant false alarm rate detector to a two-dimensional radar image, which is generated by merging a sea clutter signal at high grazing angle with a maritime target signal corresponding to the signal-to-clutter ratio. Simulation results using a computer-aided design model and commercial numerical electromagnetic solver in various scenarios show that the performance of maritime target detection significantly depends on the grazing and azimuth angles.

• The Journal of the Acoustical Society of Korea
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• v.16 no.4
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• pp.29-34
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• 1997

The prediction of detection range of a passive sonar system is essential to estimate the performance and to optimize the operation of a developed sonar system. In this paper, a model for the prediction of detection range in a range-dependent ocean environment based on the sonar equation is developed and tested. The prediction model calculates the transmission loss using PE propagation model, signal excess, and the detection probability at each target depth and range. The detection probability is integrated to give the estimated detection range. In order to validate the developed model, two cases are considered. One is the case when target depth is known. The other is the case when the target depth is unknown. The computational results agree well with the previously published results for the range-independent environment. Also,the developed model is applied to the range-dependent ocean environment where the warm eddy exists. The computational results are shown and discussed. The developed model can be used to find the optimal frequency of detection, as well as the optimal search depth for the given range-dependent ocean environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.179-186
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• 2015

This paper proposes the target path detection algorithm using statistical characteristics of an activated time lag along a moving path of target from a neighboring sensor in PDR(Pulse Doppler Radar) sensor node environment based on USN(Ubiquitous Sensor Network) with a limitation detecting only an existence of moving target. In the proposed algorithm, detection and non-detection time lag obtained from the experimental data are used. The experimental data are through repetitive action of each 500 times about three path scenarios such as passing in between two sensors, moving parallel to two sensors, and turning through two sensors. From this experiments, error detection percentages of three path scenarios are 5.67%, 5.83%, and 7.17%, respectively. They show that the proposed algorithm can exactly detect a target path using the limited PDR sensor nodes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.105-115
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• 2017

Conventional traffic detection sensors such as loop detectors and image sensors are expensive to install and maintain and require different detection algorithms depending on the night and day and have a disadvantage that the detection rate varies widely depending on the weather. On the other hand, the millimeter-wave radar is not affected by bad weather and can obtain constant detection performance regardless of day or night. In addition, there is no need for blocking trafficl for installation and maintenance, and multiple vehicles can be detected at the same time. In this study, a multi-target detection algorithm for a radar sensor with this advantage was devised / implemented by applying a conventional single target detection algorithm. We performed the evaluation and the meaningful results were obtained.