• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.6
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• pp.1091-1096
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• 2011

The main factors of radar signals used in electronic warfare are Radio Frequency(RF), Pulse Repetition Interval(PRI), Pulse Width(PW), Scan Parameter(SP) and so on. This radar signals may have some important information for the electronic warfare. So, there is a necessity for making a threat database to decide whether the radar signal is a threat or not. When the electronic support system collects some threat radar signals, it needs the search band to control the receivers and filter banks of the system. In this paper we propose search band implementation considering the type and center frequency of the receivers of the electronic support system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5527-5545
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• 2019

Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.132-140
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• 2019

In this paper, we propose a classification method for radar signals depending on the type of threat by applying machine learning to parameter data of radar signals. Currently, the army uses a library of mapping relations between the parameters and the types of threat to recognize threat signals. This approach has certain limitations when classifying signals and recognizing new types of threat or types of threat that do not exist in the current libraries. In this paper, we propose an automatic radar signal classification method depending on the type of threat that uses only parameter data without a library. A convolutional neural network is used as the classifier and machine learning is applied to train the classifier. The proposed method does not use a library, and hence, can classify threat signals that are new or do not exist in the current library.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.5
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• pp.982-984
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• 2017

In time domain, a radar signal is divided into two segments: one is for a transmitted pulse and the other is for receiving possible returns from radar targets. Also the received signal is relatively weak and consists of background noise except for the reflected signals from radar targets. In this Letter, we present an energy detection based spectrum sensing for a radar signal in the presence of noise power uncertainty exploiting this characteristics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.1-7
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• 2013

The interference among the rain radars and interference in the adjacent wireless station due to the spurious signals from the rain radar were analyzed in this paper. The rain radar measures the rain intensity using S-band signal. The measured data are utilized in forecasting the rainfall. The interference among the rain radars or in the adjacent wireless stations may be caused by the operation with low elevation angle and the high output power. Based on the propagation analysis of S band signal and the deduced interference protection ratio of rain radar, the interference due to the rain radar are analyzed. Also, the radiation spectrum characteristics of a rain radar are deduced from the caused interference effects by the spurious signals of the rain radar. To minimize the interference effects for adjacent wireless stations, it is required to get the rejection characteristics of spurious signals above 105 dB. In viewpoints of interference for rain radars, it is necessary to operate the rain radar with a different PRF and operation time opposite to adjacent rain radars.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.278-286
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• 2019

Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.725-734
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• 2016

For multi-mode radar signals in complex electromagnetic environment, different modes of one emitter tend to be deinterleaved into several emitters, called as "extension", when processing received signals by use of existing sorting methods. The "extension" problem inevitably deteriorates the sorting performance of multi-mode radar signals. In this paper, a novel method based on spatial data mining is presented to address above challenge. Based on theories of data field, we describe the distribution information of feature parameters using potential field, and makes partition clustering of parameter samples according to revealed distribution features. Additionally, an evaluation criterion based on cloud model membership is established to measure the relevance between different cluster-classes, which provides important spatial knowledge for the solution of the "extension" problem. It is shown through numerical simulations that the proposed method is effective on solving the "extension" problem in multi-mode radar signal sorting, and can achieve higher correct sorting rate.

• Journal of IKEEE
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• v.22 no.3
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• pp.624-629
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• 2018

Classification of moving targets in Pulse Doppler Radar(PDR) for surveillance and reconnaissance purposes is generally carried out based on listening and training experience of Doppler audio signals by radar operator. In this paper, we proposed the automatic classification method to identify the class of moving target with Doppler audio signals using the Mel Frequency Cepstral Coefficients(MFCC) and the Hidden Markov Model(HMM) algorithm which are widely used in speech recognition and the classification performance was analyzed and verified by simulations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.12
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• pp.1292-1299
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• 2014

Weather radar systems can provide weather information of the ground, sea, and air in extensive spatial coverage in near real time. However, it becomes problematic when ground clutter signal exists around precipitation because strong signals of ground can cause a false precipitation report. A large percentage of land coverage of Korea consists of mountainous regions where ground clutter needs to be mitigated for more accurate prediction. Thus, it is considered necessary to introduce a new suitable ground clutter removal technique specifically adequate for Korea. In this paper, the C-Map(Clutter Map) method using raw radar signals is proposed for removing ground clutter using a terrain-adaptive clutter map. A clutter map is generated using raw radar signals(I/Q) of clear days, then it is subtracted from received radar signals in frequency domain. The proposed method is applied to the radar data acquired from Sobaeksan rain radar and the result shows that the clutter rejection ratio is about 91.17 %.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.5
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• pp.590-597
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• 2016

We designed the multiple scattering points generation system to simulate an actual situation of target signal for high range resolution radar system. This provides replicating the target signals and controlling the status of target signals for radar system. This is composed transmit antenna and multi target generator. Transmit antenna is waveguide array antenna and multi target generator has signal distribution module and control & power module. Multi target generator is able to provide the high isolation and variable output power. Moreover, in order to monitor all output signals of the multi target generator, the flows of signals are programed in control & power module. The performance is demonstrated using experimented results of high range resolution radar.