• Journal of the Korea Institute of Military Science and Technology
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• v.26 no.4
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• pp.324-334
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• 2023

In this paper, the target simulator for RF signals was developed by using VST(Vector Signal Transceiver) and set by real-time signal processing SW programs. A function to process RF signals using FPGA(Field Programmable Gate Array) board was designed. The system functions capable of data processing, raw signals monitoring, target signals(simulated range, velocity) generating and RF environments data analyzing were implemented. And the characteristics of modulated signal were analyzed in RF environment. All function of programs for processing RF signal have options to store signal data and to manage the data. The validity of the signal simulation was confirmed through verification of simulated signal results.

• Journal of Ocean Engineering and Technology
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• v.11 no.3
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• pp.163-169
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• 1997

The simulation of target-scattered echo with the moving sonar platform and target in 3-dimensional ocean environment is essential to validate and evaluate the performance of a sonar system. This paper presents the improved target signal simulation on the basis of the highlight(HL) model and its realtime algorithm. In order to simulate the scattering highlight, the highlight is represented as a directional scatterer. The realtime generation algorithm of the target signal is realized by use of DSP chip, TMS320C40, where the 40 channels are equally separated to form a parallel processing task in 4 processors. The presented realtime-version of target signal simulation can be used as a target signal simulator in the development of ACM(Acoustic Counter Measure) and advanced sonar signal processing techniques.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.158.2-158
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• 2001

This paper introduces the design and implementation of a signal processing system for an airborne active homing radar system. This airborne active homing radar system uses the pulse Doppler radar of high PRF (Pulse Repetition Frequency) for computation of exact relative velocity of the target. This system carries out two operations mainly. The first is to transmit and receive microwave signal through the antenna. The second is to calculate the relative velocity of the target taking advantage of the Doppler frequency signal reflected from the target and detect the angle error between a target and an antenna LOS (Line Of Sight) to make the antenna direction coincident with the target. The signal processing system has a role of the latter.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.58-65
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• 2000

A target signal simulation method for passive sonar systems is introduced. The method uses multirate signal processing techniques to simulate moving target signals in the multi-path sound propagation environment by introducing Lloyd's mirror and Doppler effect. Time and frequency variation of target signal due to the target maneuvering is also considered to provide realistic ship signatures in the LOFAR gram so that the simulated target is used for sonar operator training. Synthesized target characteristics is analyzed and compared with real target signal in terms of interference pattern and frequency variation in the LOFAR gram.

• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.49-54
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• 2016

In this paper, the confirmation method of target detection for the vehicle mounted metal detector (MD) has been described. The vehicle mounted MD with the arrayed 6 coils to detect the width of 2.4 m was demonstrated. It is important and necessary to inform the location of the objects detected by the MD. The confirmation method of target detection was verified by using the MD GUI and the analysis of the receive signal processing. The receive signal processing is performed by comparing the threshold and the difference of the signal calibrated at initial location and the signal detected at present location.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1272-1277
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• 2010

Target detection and tracking wireless sensor network must have various signal processing ability. Wireless sensor nodes need to light weight signal processing algorithm because of energy constraints and communication bandwidth constraints. General signal processing algorithm of wireless sensor node consists of de-noising, received signal strength computation, feature extraction and signal compression. Wireless sensor network life-time and performance of target detection and classification depend on sensor node signal processing. In this paper, we propose energy efficient signal processing algorithm using wavelet transform. The proposed method estimates exact target position.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1999-2005
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• 2005

Collaboration in wireless sensor networks must be fault-tolerant due to the harsh environmental conditions in which such networks can be deployed. This paper focuses on finding signal processing algorithms for collaborative target detection based on the generalized approach to signal processing in the presence of noise that are efficient in terms of communication cost, precision, accuracy, and number of faulty sensors tolerable in the wireless sensor network. Two algorithms, namely, value fusion and decision fusion constructed according to the generalized approach to signal processing in the presence of noise, are identified first. When comparing their performance and communication overhead, decision fusion is found to become superior to value fusion as the ratio of faulty sensors to fault free sensors increases. The use of the generalized approach to signal processing in the presence of noise under designing value and decision fusion algorithms in wireless sensor networks allows us to obtain the same performance, but at low values of signal energy, as under the employment of universally adopted signal processing algorithms widely used in practice.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.203-208
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• 2010

In this paper, we convert underwater acoustic signal made by sonar system into digital image. We propose the algorithm that detects target candidate and emphasizes information of target introducing image processing technique for the digital image. The process detecting underwater target estimates background noise in underwater acoustic signal changing irregularly, recomposes it. and eliminates background from original image. Therefore, it generates initial target group. Also, it generates weighted map through proceeding doppler information, ensures information for target candidate through filtering using weighted map for image eliminated background noise, and decides the target candidate area in the single frame. In this paper, we verified that proposed algorithm almost had eliminated the noise generated irregularly in underwater acoustic signal made by simulation, targets had been displayed more surely in the image of underwater acoustic signal through filtering and process of target detection.

• Journal of IKEEE
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• v.22 no.4
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• pp.1044-1049
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• 2018

CNN (Convolutional Neural Networks) is a neural network that models animal visual information processing. And it shows good performance in various fields. In this paper, we use CNN to classify target and non-target data by analyzing the spectrogram of active sonar signal. The data were divided into 8 classes according to the ratios containing the targets and used for learning CNN. The spectrogram of the signal is divided into frames and used as inputs. As a result, it was possible to classify the target and non-target using the characteristic that the classification results of the seven classes corresponding to the target signal sequentially appear only at the position of the target signal.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.43-49
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• 2021

A small infrared image homing system is a tracking system that has an infrared image sensor that identifies a target through the day and night infrared image processing of the target on the ground and searches for and detects the target with respect to the main target. This paper describes the development of a board equipped with a high-speed CPU and FPGA (Field Programmable Gate Array) to identify target through real-time image processing by acquiring target information through infrared image. We propose a CPU-FPGA combining architecture for CPU and FPGA selection and video signal processing, and also describe a controller design using FPGA to control infrared sensor.