• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.133-140
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• 2018

In electronic warfare(EW), low probability of intercept(LPI) radar signal is a survival technique. Accordingly, identification techniques of the LPI radar waveform have became significant recently. In this paper, classification and extracting parameters techniques for 7 intrapulse modulated radar signals are introduced. We propose a technique of classifying intrapulse modulated radar signals using Convolutional Neural Network(CNN). The time-frequency image(TFI) obtained from Choi-William Distribution(CWD) is used as the input of CNN without extracting the extra feature of each intrapulse modulated radar signals. In addition a method to extract the intrapulse radar modulation parameters using binary image processing is introduced. We demonstrate the performance of the proposed intrapulse radar waveform identification system. Simulation results show that the classification system achieves a overall correct classification success rate of 90 % or better at SNR = -6 dB and the parameter extraction system has an overall error of less than 10 % at SNR of less than -4 dB.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.491-498
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• 2015

Recognition of radar emitter signals is one of core elements in radar reconnaissance systems. A novel method based on singular value decomposition (SVD) and the main ridge slice of ambiguity function (AF) is presented for attaining a higher correct recognition rate of radar emitter signals in case of low signal-to-noise ratio. This method calculates the AF of the sorted signal and ascertains the main ridge slice envelope. To improve the recognition performance, SVD is employed to eliminate the influence of noise on the main ridge slice envelope. The rotation angle and symmetric Holder coefficients of the main ridge slice envelope are extracted as the elements of the feature vector. And kernel fuzzy c-means clustering is adopted to analyze the feature vector and classify different types of radar signals. Simulation results indicate that the feature vector extracted by the proposed method has satisfactory aggregation within class, separability between classes, and stability. Compared to existing methods, the proposed feature recognition method can achieve a higher correct recognition rate.

• Journal of Ocean Engineering and Technology
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• v.37 no.1
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• pp.38-48
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• 2023

To maintain the existing systems of ships and introduce autonomous operation technology, it is necessary to improve situational awareness through the sensor fusion of the automatic identification system (AIS) and automatic radar plotting aid (ARPA), which are installed sensors. This study proposes an algorithm for determining whether AIS and ARPA signals are sent to the same ship in real time. To minimize the number of errors caused by the time series and abnormal phenomena of heterogeneous signals, a tracking method based on the combination of the unscented Kalman filter and probabilistic data association filter is performed on ARPA radar signals, and a position prediction method is applied to AIS signals. Especially, the proposed algorithm determines whether the signal is for the same vessel by comparing motion-related components among data of heterogeneous signals to which the corresponding method is applied. Finally, a measurement test is conducted on a training ship. In this process, the proposed algorithm is validated using the AIS and ARPA signal data received by the voyage data recorder for the same ship. In addition, the proposed algorithm is verified by comparing the test results with those obtained from raw data. Therefore, it is recommended to use a sensor fusion algorithm that considers the characteristics of sensors to improve the situational awareness accuracy of existing ship systems.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.4
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• pp.73-85
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• 2010

In dense electronic warfare signal environments, the conventional radar identification methods based on the basic parameters such as frequency, pulse width, and pulse repetition interval are confronted by the problem of identification ambiguity. To overcome this critical problem, a new approach introducing scan pattern of radars has been presented. Researches on new identification methods, however, suffer from a practical problem that it is not easy to secure the many radar signals including various scan pattern information and operation parameters. This paper presents a modeling method of radar signals with which we can generate radar signals including various scan pattern types according to the parameters determining the variation pattern of received signal strength. In addition, with the radar signals generated by the proposed model we analyze their characteristics according to the location of an electronic warfare support (ES) system.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.3
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• pp.221-228
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• 2020

This paper presents the radar counter jamming algorithm and ground far-field test results for the pull-off deceptive jamming signals like RGPO(Range Gate Pull Off) and VGPO(Velocity Gate Pull Off). We designed the radar counter jamming algorithm according to the characteristics of the deceptive jamming signals. This algorithm is validated by simulation before ground far-field test. The existing X-band AESA radar demonstrator was used to test the proposed algorithm. The proposed algorithm was applied to the radar processor software. The deceptive jamming signals generated using the commercial jamming signal generator. We performed the repeated ground far-field test with the test scenario. Test results show that the proposed counter deceptive jamming algorithm works in the real radar system.

• Journal of Satellite, Information and Communications
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• v.8 no.4
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• pp.159-163
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• 2013

In this paper, we propose a new method to deinterleave multi-stage stagger PRI signals of pulse radars using the electronic intelligence systems. While former algorithms were based on hardware PRI tracker only using the first deviation of the TOA of radar signals, this paper uses the first and the second deviation of TOA of radar signals and uses the PRI histogram method to deinterleave multiple PRIs of pulse radars. This algorithm can be used for deinterleaving various PRI signals at electronic intelligence systems.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.137-144
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• 2014

The Electronic Support System collects and analyzes the received radar signals in order to cope with the electronic attack in real-time. The radar-pulse clustering system classifies the radar signals that are considered to be emitted by a single source. This paper proposed a radar-pulse clustering algorithm based on four kinds of features: the direction, frequency, pulse width, and the difference of arrival time between two successive pulses. The experiment results show that the proposing algorithm could trace the moving emitter and classify the timely separated signals into different classes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5372-5378
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• 2012

In this paper, we propose a new method to quickly deinterleave the stagger PRI signals and D&S(Dwell and Switch) PRI signals from the EP radar using the electronic warfare system. While former algorithms were based on stochastic methods only using the first deviation of the TOA of radar signals, this paper uses the first and the second deviation of the TOA of radar signals to deinterleave multiple PRIs of EP radars. When we simulate multiple PRIs of EP radars and test the simulated radar signals using the proposed algorithm, various PRI signals such as fixed, jitter, D&S and stagger PRI are well deinterleaved in a short time. This algorithm is found to be very useful for electronic warfare systems.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.174-179
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• 1998

In this paper, the radar altimeter for topographic mapping over land is introduced and the characteristics of the return signals are analyzed. The radar system is described briefly and the requirements to get the fine resolution of the terrain surface height are considered. The designed radar altimeter was tested on the landscape in the near of Stuttgart. The measured data shows very fine profile of the test landscape and the height errors induced from different geometrical structure of the land surface are acquired in the measurement. In the test area, most characteristics of radar return signals over land could be tested and the results of the topographic mapping using our radar altimeter can be used for future radar altimeter development for land applications.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.277-283
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• 2023

Vital signals provide essential information regarding the health status of individuals, thereby contributing to health management and medical research. Present monitoring methods, such as ECGs (Electrocardiograms) and smartwatches, demand proximity and fixed postures, which limit their applicability. To address this, Non-contact vital signal measurement methods, such as CW (Continuous-Wave) radar, have emerged as a solution. However, unwanted signal components and a stepwise processing approach lead to errors and limitations in heart rate detection. To overcome these issues, this study introduces an integrated neural network approach that combines noise removal, demodulation, and dominant-frequency detection into a unified process. The neural network employed for signal processing in this research adopts a MLP (Multi-Layer Perceptron) architecture, which analyzes the in-phase and quadrature signals collected within a specified time window, using two distinct input layers. The training of the neural network utilizes CW radar signals and reference heart rates obtained from the ECG. In the experimental evaluation, networks trained on different datasets were compared, and their performance was assessed based on loss and frequency accuracy. The proposed methodology exhibits substantial potential for achieving precise vital signals through non-contact measurements, effectively mitigating the limitations of existing methodologies.