• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.29 no.6
• /
• pp.433-440
• /
• 2018

A radar receives reflected signals from various objects to detect a target. Undesired object, called clutter, as well as the target generates reflected signals. The clutter radar cross section(RCS) is dependent on many factors, which are the antenna pattern, distance between the radar and the target, and the height of the target and the radar. Herein, surface clutter RCS for ground-based radar is analyzed, and the effect of the surface clutter RCS on the received signal is investigated.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.2
• /
• pp.170-178
• /
• 2019

Classification of radar signals in the field of electronic warfare is a problem of discriminating threat types by analyzing enemy threat radar signals such as aircraft, radar, and missile received through electronic warfare equipment. Recent radar systems have adopted a variety of modulation schemes that are different from those used in conventional systems, and are often difficult to analyze using existing algorithms. Also, it is necessary to design a robust algorithm for the signal received in the real environment due to the environmental influence and the measurement error due to the characteristics of the hardware. In this paper, we propose a radar signal classification method which are not affected by radar signal modulation methods and noise generation by using deep learning techniques.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.19 no.2
• /
• pp.47-55
• /
• 2023

Recently, Sensor technologies are emerging to prevent traffic accidents and support safe driving in complex environments where human perception may be limited. The UWS is a technology that uses an ultrasonic sensor to detect objects at short distances. While it has the advantage of being simple to use, it also has the disadvantage of having a limited detection distance. The LDWS, on the other hand, is a technology that uses front image processing to detect lane departure and ensure the safety of the driving path. However, it may not be sufficient for determining the driving environment around the vehicle. To overcome these limitations, a system that utilizes FMCW radar is being used. The BSD radar system using FMCW continuously emits signals while driving, and the emitted signals bounce off nearby objects and return to the radar. The key technologies involved in designing the BSD radar system are tracking algorithms for detecting the surrounding situation of the vehicle. This paper presents a tracking algorithm for designing a BSD radar system, while explaining the principles of FMCW radar technology and signal types. Additionally, this paper presents the target tracking procedure and target filter to design an accurate tracking system and performance is verified through simulation.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.3
• /
• pp.497-502
• /
• 2018

An analog to digital converter(: ADC) has been designed to extract video signals of marine X-band radar and convert to digital signals in order to produce rainfall information. X-band weather radars are suitable for high temporal-spatial resolution observations of rainfall over local ranges but they are very expensive and require professional management. The marine radars with 10-2 cost facilitate data collection and management as well as economic benefits. To validate the usefulness of the developed ADC, comparative observations were made with weather radar for short term precipitation cases. The rainfall distribution of marine radar observations are consistent with that of weather radar within a radius of 15 km. This demonstrates the usability of marine radar for rainfall observations.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.26 no.5
• /
• pp.384-391
• /
• 2023

In modern electronic warfare, a number of radar emitters are in operation, causing radar receivers to receive high-density signal pulses that occur simultaneously. To analyze the radar signals more accurately and identify enemies, the sorting process of high-density radar signals is very important before analysis. Recently, machine learning algorithms, specifically K-means clustering, are the subject of research aimed at improving the accuracy of radar signal sorting. One of the challenges faced by these studies is that the clustering results can vary depending on how the initial points are selected and how many clusters number are set. This paper introduces a repeated K-means clustering algorithm that aims to accurately cluster all data by identifying and addressing false clusters in the radar sorting problem. To verify the performance of the proposed algorithm, experiments are conducted by applying it to simulated signals that are generated by a signal generator.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.29 no.1
• /
• pp.61-67
• /
• 2018

The effects of subsurface inhomogeneities on the detection of buried metal pipes in ground-penetrating radar(GPR) signals are investigated numerically. To model the electrical properties of the subsurface inhomogeneities, the continuous random media(CRM) generation technique is introduced. For the electromagnetic simulation of GPR signals, the finite-difference time-domain(FDTD) method is implemented. As a function of the standard deviation and the correlation length of the relative permittivity distribution for a randomly inhomogeneous ground, the GPR signals of the buried metal pipes are compared using numerical simulations. As the subsurface inhomogeneities increase, the GPR signals of the buried pipes are distorted because of the effect of the subsurface clutter.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.21 no.4
• /
• pp.331-339
• /
• 2010

To analyze and verify the performance of a MWR(Missile Warning Radar) of a GVWS(Ground Vehicle Weapon System), there is a need to make a simulator which can conduct the linked and engaged test virtually using the simulated signals. In this paper, a method of the simulator design for MWR is proposed to solve the above need. The SP(Signal Processor) part which generates the simulated signals and analyzes the algorithms is explained. The RF(Radio Frequency) part which transforms IF(Intermediate Frequency) signals into RF signals, radiates RF signals, and controls the linked equipments is also explained. The utility of the proposed design is proved by presenting the results of the contributions to the development of MWR.

• Journal of IKEEE
• /
• v.22 no.1
• /
• pp.162-167
• /
• 2018

In this paper, we propose a method to classify radar signals according to the jamming technique by applying the machine learning to parameter data extracted from received radar signals. In the present army, the radar signal is classified according to the type of threat based on the library of the radar signal parameters mostly built by the preliminary investigation. However, since radar technology is continuously evolving and diversifying, it can not properly classify signals when applying this method to new threats or threat types that do not exist in existing libraries, thus limiting the choice of appropriate jamming techniques. Therefore, it is necessary to classify the signals so that the optimal jamming technique can be selected using only the parameter data of the radar signal that is different from the method using the existing threat library. In this study, we propose a method based on machine learning to cope with new threat signal form. The method classifies the signal corresponding the new jamming method for the new threat signal by learning the classifier composed of the hidden Markov model and the neural network using the existing library data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.553-556
• /
• 2012

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 high output power of rain radar. 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.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.12 no.2
• /
• pp.71-77
• /
• 2017

This paper proposes a low complexity frequency modulated continuous wave (FMCW) surveillance radar algorithm. In the conventional surveillance radar systems, the two dimensional (2D) fast Fourier transform (FFT) method is usually employed in order to detect the distance and velocity of the targets. However, in a surveillance radar systems, it is more important to immediately detect the presence or absence of the targets, rather than accurately detecting the distance or speed information of the target. In the proposed algorithm, in order to immediately detect the presence or absence of targets, 1D FFT is performed on the first and M-th bit signals among a total of M beat signals and then a phase change between two FFT outputs is observed. The range of target is estimated only when the phase change occurs. By doing so, the proposed algorithm achieves a significantly lower complexity compared to the conventional surveillance scheme using 2D FFT. In addition, show in order to verify the performance of the proposed algorithm, the simulation and the experiment results are performed using 24GHz FMCW radar module.