• ETRI Journal
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• v.40 no.2
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• pp.188-196
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• 2018

This paper presents a pulse radar system to detect drones based on a target fluctuation model, specifically the Swerling target model. Because drones are small atypical objects and are mainly composed of non-conducting materials, their radar cross-section value is low and fluctuating. Therefore, determining the target fluctuation model and applying a proper integration method are important. The proposed system is herein experimentally verified and the results are discussed. A prototype design of the pulse radar system is based on radar equations. It adopts three different pulse modes and a coherent pulse integration to ensure a high signal-to-noise ratio. Outdoor measurements are performed with a prototype radar system to detect Doppler frequencies from both the drone frame and blades. The results indicate that the drone frame and blades are detected within an instrumental maximum range. Additionally, the results show that the drone's frame and blades are close to the Swerling 3 and 4 target models, respectively. By the analysis of the Swerling target models, proper integration methods for detecting drones are verified and can thus contribute to increasing in detectability.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.1
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• pp.92-102
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• 2017

For an automotive surveillance radar system, fast-chirp train based FMCW (Frequency Modulated Continuous Wave) radar is a very effective method, because clutter and moving targets are easily separated in a 2D range-velocity map. However, pedestrians with low echo signals may be masked by strong clutter in actual field. To address this problem, we proposed in the previous work a clutter cancellation and moving target indication algorithm using the coherent phase method. In the present paper, we initially composed the test set-up using a 24 GHz FMCW transceiver and a real-time data logging board in order to verify this algorithm. Next, we created two indoor test environments consisting of moving human and stationary targets. It was found that pedestrians and strong clutter could be effectively separated when the proposed method is used. We also designed and implemented these algorithms in FPGA (Field Programmable Gate Array) in order to analyze the hardware and time complexities. The results demonstrated that the complexity overhead was nearly zero compared to when the typical method was used.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.3
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• pp.612-621
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• 1996

In this paper, we propose the low sidelobe digital FIR doppler filter bank synthesis algorithm through the Gradient Descent method and it can be practially appliable to coherent pulse doppler radar signal processing. This algorithm shows the appropriate calculation of tap coefficients or zeros for FIR transversal fiter which has been employed in radar signal processor. The span of the filters in the filter bank be selected at the desired position the designer want to locate, and the lower sidelobe level that has equal ripple property is achieved than one for which the conventional weithtedwindow is used. Especially, when we implemented filter zeros as design parameters it is possible to make null filter gain at zero frequency intensionally that would be very efficient for the eliminatio of ground clutter. For the example of 10 tap filter synthesis, when filter coefficients or zeros are selected as design parameters the corresponding sidelobelevel is reducedto -70db or -100db respectively and it has good convergent characteristics to the desired sidelobe reference value. The accuracy ofapproach to the reference value and the speed of convergence that show the performance measure of this algorithm are tuned out with some superiority and the fact that the bandwidth of filter appears small with respect to one which is made by conventional weighted window method is convinced. Since the filter which is synthesized by this algorithm can remove the clutter without loss of target signal it strongly contributes performance improvement with which detection capability would be concerned.

• Journal of IKEEE
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• v.28 no.3
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• pp.397-404
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• 2024

This paper presents the design and implementation results of a Raspberry-Pi-based embedded system with an FPGA accelerator that can detect and classify objects using an FMCW radar sensor for preventing door collision accidents in vehicles. The proposed system performs a radar sensor signal processing and a deep learning processing that classifies objects into bicycles, automobiles, and pedestrians. Since the CNN algorithm requires substantial computation and memory, it is not suitable for embedded systems. To address this, we implemented a lightweight deep learning model, BNN, optimized for embedded systems on an FPGA, and verified the results achieving a classification accuracy of 90.33% and an execution time of 20ms.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.443-461
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• 2022

Target Detection in synthetic aperture radar(SAR) image is critical for military and national defense. In this paper, we propose YOLOv4-Attention architecture which adds attention modules to YOLOv4 backbone architecture to complement the feature extraction ability for SAR target detection with high accuracy. For training and testing our framework, we present new SAR embedding datasets based on MSTAR SAR public datasets which are about poor environments for target detection such as various clutter, crowded objects, various object size, close to buildings, and weakness of signal-to-clutter ratio. Experiments show that our Attention YOLOv4 architecture outperforms original YOLOv4 architecture in SAR image target detection tasks in poor environments for target detection.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.151-157
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• 2019

Recently, monitoring respiration of people has been of interest using non-invasive method. Among the vital signals usually used for indicating health status, non-invasive and portable device based monitoring respiratory status is practically useful and enable one to promptly deal with abnormal physical status. This paper proposes the approach to real-time detection of apnea signal based on Short-Time-Fourier-Transform(STFT). Contrary to the analysis of a signal in frequency domain using Fast-Fourier Transform, this paper employs Short-time-Fourier-Transform so that frequency response can be analyzed in short time interval. The respiratory signal is acquired using UWB radar sensor that enables one to obtain respiration signal in contactless way. Detection of respiratory status is carried out by analyzing frequency response, and classification of respiratory status can be provided. In particular, STFT is employed to analyze respiratory signal in real-time, leading to effective analysis of the respiratory status in practice. In the case of existence of noise in the signal, appropriate filtering process is employed as well. The proposed method is straightforward and is workable in practice to analyze the respiratory status of people. To evaluate the proposed method, experimental results are provided.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.2
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• pp.125-132
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• 2003

This study introduces a simulation model of radar responses from subsurface voids in concrete for their frequency variations. In this model, resolution and attenuation characteristics due to frequency variations are analyzed at each material interface which has different electromagnetic property. This model aims at the selection of best frequency of radar which can analyze the thickness of voids in concrete from radar responses. It can also be applied to estimate the limitation of propagation depth of radar on subsurface voids in concrete. The computed results show the radar images obtained by using a radar signal processing technique using convolution.

• ETRI Journal
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• v.41 no.3
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• pp.316-325
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• 2019

Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation (LFM) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.

• Journal of the Korean Recycled Construction Resources Institute
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• v.5 no.2
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• pp.177-184
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• 2017

Many ground subsidence accidents have happened in Korea. The accident was caused by the subsidence and leakage of the deteriorated sewage pipe. This study aims to establish the empirical data of the ground penetration radar(GPR) detection for ground subsidence. A test bed was also manufactured for the same purpose. The GPR detection variables are embedment depth and horizontal distance of embedded cast iron pipe and expanded polystyrene(EPS). From the detection results, the EPS embedded by a depth of 1.5m was difficult for detection. The EPS closely embedded to the cast iron pipe within a 0.5m distance had a very strong cast iron pipe signal. Therefore, the detection was impossible. This study developed an image processing program, called the GPR image processing program(GPRiPP), to process the GPR detection results. Its major function is the gain function, which amplifies the wiggle wave signal. Compared to the existing programs, the GPRiPP is capable of showing a similar image processing performance.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.5
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• pp.571-576
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• 2015

This paper presents a comparative study of wave height estimation method that was used for signal to noise ratio and shadowing ratio based on X-band marine radar. If the signal to noise ratio, and is widely used as a method for estimating an wave height, a new method is presented for shadowing ratio. In the case of radar images used in this study it is measuring the data from the coast of Ulsan Jujeon, compared with marine meteorological information from the Meteorological Agency measured a light beacon. We compared the measured data for about 34 days, the typhoon was measured, incluidng a period in the East Sea, and verify the results for various distribution of wave height. For estimate wave height using a shadowing ratio analysis, it does not require calibration and real-time advantages of this part, coming confirmed the possibility of the measurement, the cause detection error for radar image was caused due to determine.