• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• Transactions of the Korean Society of Automotive Engineers
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• v.18 no.4
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• pp.54-61
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• 2010

In this paper, we design the signal processing unit to effectively support the proposed algorithm for an automotive Frequency Modulation Continuous Wave(FMCW) radar. In the proposed method, we can obtain the distance and velocity with improved error depending on each range(long, middle, and short) of the target. Since a high computational capacity is required to obtain more accurate distance and velocity for target in near range, the proposed signal processing unit employs the time de-interleaving and the frequency interpolation method to overcome the limitation. Moreover, for real-time signal processing, the parallel architecture is used to extract simultaneously the distance and velocity in each range.

• 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.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.372-381
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• 2014

This study examined a radar signal active cancelation technique, which is a theoretical way of achieving stealth by employing a baseband process that involves sampling the incoming hostile radar signal, analyzing its characteristics, and generating countermeasure signals to cancel out the linear-FM signal of the hostile radar signal reflected from the airborne target. To successfully perform an active cancelation, the effects of errors in the countermeasure signal were first analyzed. To generate the countermeasure signal that requires very fast and accurate processing, the down-sampling technique with the suboptimal maximum likelihood estimation (sMLE) scheme was proposed to improve the speed of the estimation process while preserving the estimation accuracy. The simulation results showed that the proposed down-sampling technique using a 2048 FFT size yields substantial power reduction despite its small FFT size and exhibits similar performance to the sMLE scheme using the 32768 FFT size.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.114-122
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• 2020

When replacing hardware due to obsolescence, discontinuation, and expansion of software-equipped electronic equipment, software changes are required in the past, but if virtualization technology is applied, it can be applied without software changes. In this regard, we studied in order to apply virtualization technology in the development of naval multi-function radar signal processing, we studied hardware and OS independency for Docker and performance comparison between Docker and virtual machine. As a result, it was confirmed that hardware and OS independence exist when using Docker and that high-speed processing is possible compared to the virtual machine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2628-2635
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• 2010

This paper analyzes the detection probability of FMCW (Frequency Modulated Continuous Wave) radar based on Doppler frequency and analog-digital converter bit and designs and implements signal processing part of FMCW radar. For performance evaluation, the FMCW radar system consists of a transmitted part and a received part and uses AWGN channel. The system model is verified through analysis and simulation. Frequency offset occurs in the received part caused by the mismatching between the received signal and the reference signal. In case of Doppler frequency less than about 38KHz, performance degradation of detection does not occur in FMCW radar with 75cm resolution The analog-digital converter needs at least 6 bit in order not to degrade the detection probability. And, we design and implement digital signal processing part based on DDS chip of digital transmitted signal generator for FMCW radar.

• Journal of Advanced Navigation Technology
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• v.23 no.2
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• pp.142-150
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• 2019

Muzzle Velocity is one of the most important measurement items for evaluation of ammunition. The muzzle velocity is defined as the velocity when the projectile leaves the muzzle. Particularly, since the muzzle velocity is closely related to the performance of the propellant, precise measurement of muzzle velocity is required. Doppler radar is used to measure the muzzle velocity, but the quality of Doppler radar signal depends on the test site environment. In this paper, a method to remove the clutter that degrades the signal quality of Doppler radar by improving the structure of the test site and the signal processing method is suggested. For the application of the improved signal processing method, a program for acquiring Doppler radar's raw Doppler data was created. Statistical verification of the velocity data obtained through the improvement of the test site structure and signal processing method proved that the proposed method is effective for the removal of clutter as compared with the existing method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.41-46
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• 2023

It is very important to meet the maximum detection range in a radar system. In order to meet the maximum detection Range, the sensitivity of the received signal of the radar system must be high. In addition, the dynamic range should be wide in the radar signal processing board. To meet these requirements, the signal processing board must be designed to be robust against external and internal noise. In particular, a design is required to minimize the effect of noise generated by various switching circuits inside the board on the received radar signal. In this paper, we derive the requirements of the signal processor board to meet the radar system performance and describe the design to meet the derived requirements. In addition, the EMC design to minimize the influence of noise input from the outside or generated from the inside is described. Confirm the secured performance through the test of the manufactured board.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.357-359
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• 2009

In this paper, the 2.4GHz doppler radar system consisting of the doppler radar module and a baseband module were designed to detect heartbeat and respiration signal without direct skin contact. A bio-radar system emits continuous RF signal of 2.4GHz toward human chest, and then detects the reflected signal so as to investigate cardiopulmonary activities. The heartbeat and respiration signals acquired from quadrature signal of the doppler radar system are applied to the pre-processing circuit, amplification circuit, and the offset circuit of the baseband module. ECG(electrocardiogram) and reference respiration signals are measured simultaneously to evaluate the doppler radar system. As a result, the respiration signal of doppler radar signal is detected to 1m without complex digital signal processing. The sensitivity and calculated from I/Q respiration signal were $98.29{\pm}1.79%$, $97.11{\pm}2.75%$, respectively, and positive predictivity were $98.11{\pm}1.45%$, $92.21{\pm}10.92%$, respectively. The sensitivity and positive predictivity calculated from phase and magnitude of the doppler radar were $95.17{\pm}5.33%$, $94.99{\pm}5.43%$, respectively. In this paper, we confirmed that noncontact real-time heartbeat and respiration detection using the doppler radar system has the possibility and limitation.

• 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.