Configuration of an X-band FMCW radar targeted for drone detection

Park, Seungwoon

doi:10.1109/isanp.2017.8228912

Cited by 19 publications

(8 citation statements)

References 2 publications

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“…We can transform the two dimension to one dimension through DR, which can separate TOA and the AOA parameters, by using (11). We first determine the TOAs by the DFT results of the received signal, as in [16].…”

Section: Proposed Joint Toas and Aoas Using Dr And Combination Ofmentioning

confidence: 99%

“…For a very long time, frequency modulated continuous-wave (FMCW) radar systems are widely utilized for a variety of fields in order to obtain the TOAs and AOAs of moving targets. Especially, phase array beamforming based on vehicle radar sensor systems [2] is used for smart cruise control, traffic monitoring and collision avoidance [3]- [6] and airborne radar is utilized civil and military applications with high-resolution detection and small size system [7]- [11]. By a de-chirping method of an FMCW radio frequency (RF) module, the received signals are able to be transformed into sinusoidal waveforms in order to acquire TOAs and AOAs information.…”

Section: Introductionmentioning

confidence: 99%

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A Low-Complexity Joint TOAs and AOAs Parameter Estimator Using Dimension Reduction for FMCW Radar Systems

Kim¹,

Ju²,

Lee³

2018

ElAEE

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We propose a low-complexity of joint time delay of arrivals (TOAs) and angles of arrivals (AOAs) using dimension reduction for frequency modulated continuous-wave (FMCW) radar systems. In the road environment, the FMCW radar can obtain the position of other vehicles accurately since the radar system is able to estimate spatial-temporal parameters such as TOAs and AOAs of multiple targets. The requirement of FMCW radar is increasing in terms of the parameter accuracy and resolution over time. In order to accomplish high accuracy and resolution of the parameters such as range, velocity, and angle, conventional two dimensional-discrete Fourier transform (2D-DFT) have reduced performance of the parameter estimation. To enhance the estimation performance of parameter, proposed high resolution based methods, which are two dimensional-multiple signal classification (2D-MUSIC), is utilized. However, conventional high-resolution methods are not used in real-time systems due to their high complexity situation. In order to meet resolution ability and automotive radar system requirements, we present a joint TOAs and AOAs parameter estimator using dimension reduction with combination of the DFT and the MUSIC algorithm for low-complexity FMCW radar systems. In experimental results of multi-target environments, in terms of the estimation performance, the proposed method is better than 2D-MUSIC.

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Section: Proposed Joint Toas and Aoas Using Dr And Combination Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Low-Complexity Joint TOAs and AOAs Parameter Estimator Using Dimension Reduction for FMCW Radar Systems

Kim¹,

Ju²,

Lee³

2018

ElAEE

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“…Compared with the pulse radar, dechirp-receiving radar equipment is simple and inexpensive. Therefore, it is widely used, such as detecting unmanned aerial vehicles (UAVs) with X-band LFMCW radars [3,4], designing an X-band LFMCW radar for automobile anticollision [5], monitoring vehicles with a Ka-band LFMCW inverse synthetic aperture radar (ISAR) [6], tracking a corner reflector held by a walking person with a Ka-band LFMCW interferometric ISAR [7], and imaging simulation of space targets with an X-band LFMCW ISAR [8].…”

Section: Introductionmentioning

confidence: 99%

Dechirp‐receiving radar target detection based on generalized Radon‐Fourier transform

You

Ding

Liu

et al. 2021

IET Radar Sonar & Navi

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The dechirp-receiving radar measures range by the frequency difference between the target echo and the reference signal. It can operate at a low sampling frequency while transmitting a wideband signal, which simplifies the radar hardware. However, when detecting high-speed and highly maneuvering targets, residual video phase (RVP), frequency mismatch, frequency modulation (FM) rate mismatch, and across range-Doppler unit (ARDU) problems occur after dechirp receiving, which cause difficulties in integrating the target echo and result in severe performance loss. To solve these problems, the dechirp generalized Radon-Fourier transform (DGRFT) and an algorithm for its fast implementation are proposed. The DGRFT compensates for the frequency and FM rate mismatch in the fast-time domain and the RVP in the fast-time frequency domain, and then implements joint envelope and phase compensations to overcome the ARDU phenomenon. After these compensations, the target echo can be effectively integrated. Experiments with simulated and measured data show that the performance of the DGRFT is better than that of the conventional moving target detector.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…UAVs in operation are conventionally detected by radar, visual detection or acoustic sensors, as shown in Table 1. Park and Park [19] discussed the detection of UAVs with Frequency-Modulated Continuous-Wave (FMCW) Radar system, and the UAV signal was detected up to more than 500 m of distance in real-time with the error of less than 0.1%. However, the cost of radar detection is relatively high, and it is difficult to detect at a lower height.…”

Section: Introductionmentioning

confidence: 99%

Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles

Zhao

Chen

et al. 2018

Applied Sciences

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Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), this paper explores the feasibility of using GAN to improve the training datasets and obtain a better classification model, thereby improving the accuracy of classification. First, we use the generative model of GAN to generate a large datasets, which does not need manual annotation. At the same time, the discriminative model of GAN is improved to classify the types of signals based on the loss function of the discriminative model. Finally, this model can be used to the outdoor environment and obtain a real-time illegal UAVs signal classification system. Our experiments confirmed that the improvements on the Auxiliary Classifier Generative Adversarial Networks (AC-GANs) by limited datasets achieve excellent results. The recognition rate can reach more than 95% in the indoor environment, and this method is also applicable in the outdoor environment. Moreover, based on the theory of Wasserstein GANs (WGAN) and AC-GANs, a more robust Auxiliary Classifier Wasserstein GANs (AC-WGANs) model is obtained, which is suitable for multi-class UAVs. Through the combination of AC-WGANs and Universal Software Radio Peripheral (USRP) B210 software defined radio (SDR) platform, a real-time UAVs signal classification system is also implemented.

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Configuration of an X-band FMCW radar targeted for drone detection

Cited by 19 publications

References 2 publications

A Low-Complexity Joint TOAs and AOAs Parameter Estimator Using Dimension Reduction for FMCW Radar Systems

A Low-Complexity Joint TOAs and AOAs Parameter Estimator Using Dimension Reduction for FMCW Radar Systems

Dechirp‐receiving radar target detection based on generalized Radon‐Fourier transform

Application of Auxiliary Classifier Wasserstein Generative Adversarial Networks in Wireless Signal Classification of Illegal Unmanned Aerial Vehicles

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