Range-Doppler Map Improvement in FMCW Radar for Small Moving Drone Detection Using the Stationary Point Concentration Technique

Park, Jun-Hyeong; Jung, Dae-Sung; Bae, Kyung-Bin; Park, Seong‐Ook

doi:10.1109/tmtt.2019.2961911

Cited by 24 publications

(8 citation statements)

References 36 publications

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“…In the literature [18,19], a longer observation interval is used in exchange for a smaller scanning bandwidth in SAR radar imaging, which gains a higher range resolution. Reference [20] reduces 2D noise by reducing leakage in FMCW radar and eliminates unnecessary doppler shift by phase calibration. Reference [21] lightens noise in ISAR images by transforming the original image into the image domain and extracting the strongest scattering points.…”

Section: Gnss Radar Moving Target Rdm Imaging Principlementioning

confidence: 99%

Enhanced Range Doppler Mapping Algorithm for Passive GNSS based radar Rerial Target Detection

Zhang,

Zheng,

Zheng

et al. 2023

Preprint

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Passive GNSS based radar is a developing non-contact and emerging remote sensing technology in recent decade. However, due to the problems of weak signal and low resolution, the application of such technology in remote sensing application is very limited. This paper considers GNSS based radar detection of air object, and puts forward the signal-to-noise ratio improvement scheme of range doppler map (RDM) to improve the detection ability. In this paper, bi-static GNSS radar model is employed. The RDM of air target is obtained on the basis of direct and reflected signals. The proposed RDM SNR (signal-to-noise ratio) improvement algorithm mainly uses the autocorrelation characteristics of GNSS code to purify and reduce the noise of GNSS signal, so as to improve resolution of RDM and accuracy of target motion state solution. Compared with the current noise reduction method, which mostly depends on the prior conditions such as background environment, the proposed algorithm is more applicable, as it is mainly based on the characteristics of the signal. The RDM image is obtained by simulating moving target detection.In the condition of input signal CNR(Carrier-to-noise ration) is 35dB, the algorithm shows that the image information entropy after noise reduction is 63.23% for GPS signal and 71.69% for Beidou signal which lower than that before noise reduction.

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Section: Gnss Radar Moving Target Rdm Imaging Principlementioning

confidence: 99%

Enhanced Range Doppler Mapping Algorithm for Passive GNSS based radar Rerial Target Detection

Zhang,

Zheng,

Zheng

et al. 2023

Preprint

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“…Monitoring the illegal flying of drones and the prevention of bird strikes have become challenging problems for several applications, e.g., airport clearance zone surveillance, important event or place security, etc. [4][5][6]. One of the key technologies is classification of the two kinds of targets, as people need to tell them apart for the following different precautions.…”

Section: Introductionmentioning

confidence: 99%

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

et al. 2022

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Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

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“…Heterodyne FMCW radar has frequently been employed to resolve the problem of dc offsets 9‐12 . However, as the heterodyne FMCW radar usually uses different local oscillators (LOs), it causes uncorrelated phase noise (UPN) and the RCE has little or no effect, which results in an increase in the noise floor 10 .…”

Section: Introductionmentioning

confidence: 99%

“…However, as the heterodyne FMCW radar usually uses different local oscillators (LOs), it causes uncorrelated phase noise (UPN) and the RCE has little or no effect, which results in an increase in the noise floor 10 . When the configuration of the radar is monostatic or quasi‐monostatic, the leakage signal, which is a drawback of the FMCW radar, becomes intensely strong and can worsen the noise floor rise 11,12 . However, in these configurations, it is unclear how the UPN of the leakage signal affects the noise floor, especially over the middle‐long range, in the final signal processing result of the heterodyne FMCW radar.…”

Section: Introductionmentioning

confidence: 99%

Strategic method of determining parameter values in frequency modulated continuous wave radar for low noise floor over middle‐long range

Park

Jung

Park

2021

Micro & Optical Tech Letters

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Heterodyne architecture has been frequently chosen to resolve the problem of dc offset in frequency modulated continuous wave (FMCW) radar. However, heterodyne FMCW radars use different local oscillators, resulting in uncorrelated phase noise (UPN) in beat signals. Therefore, the inherent leakage signal in the heterodyne FMCW radar also has UPN and raises the noise floor in the power spectrum. In this letter, we propose a strategic decision method for parameter values in the heterodyne FMCW radar to achieve a low noise floor over the middle‐long range. In addition, we experimentally discover the relation between the UPN and the processing gain for the first time. Based on this relation, we devise an exact formula for the degree of improvement achieved due to the proposed strategic decision method. Experimental results confirm the devised formula and show that the proposed method significantly reduces the noise floor over the middle‐long range.

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Range-Doppler Map Improvement in FMCW Radar for Small Moving Drone Detection Using the Stationary Point Concentration Technique

Cited by 24 publications

References 36 publications

Enhanced Range Doppler Mapping Algorithm for Passive GNSS based radar Rerial Target Detection

Enhanced Range Doppler Mapping Algorithm for Passive GNSS based radar Rerial Target Detection

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

Strategic method of determining parameter values in frequency modulated continuous wave radar for low noise floor over middle‐long range

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