scholarly journals A FOD Detection Approach on Millimeter-Wave Radar Sensors Based on Optimal VMD and SVDD

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 997
Author(s):  
Jun Zhong ◽  
Xin Gou ◽  
Qin Shu ◽  
Xing Liu ◽  
Qi Zeng
Keyword(s):  
Millimeter Wave ◽  
Detection Methods ◽  
Economic Losses ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Range Resolution ◽  
Radar Sensors ◽  
Detection Approach ◽  
Wave Radar ◽  
Whale Optimization

Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.

scholarly journals Theoretical and Experimental Study on Echo Fluctuation Suppression of a Cirrus Cloud by Millimeter Wave MIMO Radar

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Jinhu Wang ◽  
Junxiang Ge ◽  
Ming Wei ◽  
Hongbin Chen ◽  
Zexin Yang ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Mimo Radar ◽  
Physical Parameters ◽  
Cirrus Cloud ◽  
Nonspherical Particles ◽  
Range Resolution ◽  
Rayleigh Approximation ◽  
Ice Particles ◽  
Wave Radar ◽  
Classical Vector

The scattering properties of nonspherical particles can be approximately computed by equivalent spherical theory. The scattering properties of ice particles were approximately computed by Rayleigh approximation because the sizes of the ice particles are smaller than the wavelength of millimeter wave radar. Based on the above assumption, the echo fluctuation of moving particles was analyzed by computing the total backscattering field of a cirrus cloud using the classical vector potential technique. The simulation results showed that echo fluctuation influences the accuracy of retrieving the physical parameters of a cloud. To suppress the echo fluctuation of moving ice particles, a video integrator of a millimeter wave cloud radar would be used. However, video integrators lose the rapidly changing information of ice particles and reduce radar range resolution; thus, we propose the pace-diversity technique of MIMO radar to reduce the echo fluctuation, which could be validated by theoretical computation and experimental measurements.

scholarly journals Remote Monitoring of Human Vital Signs Based on 77-GHz mm-Wave FMCW Radar

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2999 ◽  
Author(s):  
Yong Wang ◽  
Wen Wang ◽  
Mu Zhou ◽  
Aihu Ren ◽  
Zengshan Tian
Keyword(s):  
Millimeter Wave ◽  
Human Body ◽  
Continuous Wave ◽  
Intermediate Frequency ◽  
Detection Methods ◽  
Discrete Wavelet ◽  
Phase Information ◽  
Millimeter Wave Radar ◽  
Wave Radar ◽  
Radar Sensing

In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has gradually received attention in the field of radar sensing. Therefore, this paper extends the application of millimeter-wave radar to the field of health care. The millimeter-wave radar first transmits the frequency-modulated continuous wave (FMCW) and collects the echo signals of the human body. Then, the phase information of the intermediate frequency (IF) signals including the breathing and heartbeat signals are extracted, and the Direct Current (DC) offset of the phase information is corrected using the circle center dynamic tracking algorithm. The extended differential and cross-multiply (DACM) is further applied for phase unwrapping. We propose two algorithms, namely the compressive sensing based on orthogonal matching pursuit (CS-OMP) algorithm and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) algorithm, to separate and reconstruct the breathing and heartbeat signals. Then, a frequency-domain fast Fourier transform and a time-domain autocorrelation estimation algorithm are proposed to calculate the respiratory and heartbeat rates. The proposed algorithms are compared with the contact-based detection ones. The results demonstrate that the proposed algorithms effectively suppress the noise and harmonic interference, and the accuracies of the proposed algorithms for both respiratory rate and heartbeat rate reach about 93%.

scholarly journals Humanrecognition Using ‘Pso-Ofa’ In Low Resolution Videos

2021 ◽  
Vol 12 (11) ◽  
pp. 697-703
Author(s):  
K.Ranga Narayana, Et. al.
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Previous Method ◽  
Video Data ◽  
Detection Methods ◽  
Support Vector ◽  
Low Resolution ◽  
Swarm Optimization ◽  
Detection Approach ◽  
Novel Method

In present scenario, tracking of target in videos with low resolution is most important task.  The problem aroused due to lack of discriminatory data that have low visual visibility of the moving objects. However, earlier detection methods often extract explanations around fascinating points of space or exclude mathematical features in moving regions, resulting in limited capabilities to detect better video functions. To overcome the above problem, in this paper a novel method which recognizes a person from low resolution videos is proposed. A Three step process is implemented in which during the first step, the video data acquired from a low-resolution video i.e. from three different datasets. The acquired video is divided into frames and converted into gray scale from RGB. Secondly, background subtraction is performed using LBP and thereafter Histogram of Optical Flow (HOF) descriptors is extracted from optical flow images for motion estimation. In the third step, the eigen features are extracted and optimized using particle swarm optimization (PSO) model to eliminate redundant information and obtain optimized features from the video which is being processed. Finally to find a person from low resolution videos, the features are classified by Support Vector Machine (SVM) and parameters are evaluated. Experimental results are performed on VIRAT, Soccer and KTH datasets and demonstrated that the proposed detection approach is superior to the previous method

scholarly journals Finding new physics without learning about it: anomaly detection as a tool for searches at colliders

2021 ◽  
Vol 81 (1) ◽  
Author(s):  
M. Crispim Romão ◽  
N. F. Castro ◽  
R. Pedro
Keyword(s):  
Anomaly Detection ◽  
New Physics ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Detection Techniques ◽  
Learning Techniques ◽  
New Strategy

AbstractIn this paper we propose a new strategy, based on anomaly detection methods, to search for new physics phenomena at colliders independently of the details of such new events. For this purpose, machine learning techniques are trained using Standard Model events, with the corresponding outputs being sensitive to physics beyond it. We explore three novel AD methods in HEP: Isolation Forest, Histogram-Based Outlier Detection, and Deep Support Vector Data Description; alongside the most customary Autoencoder. In order to evaluate the sensitivity of the proposed approach, predictions from specific new physics models are considered and compared to those achieved when using fully supervised deep neural networks. A comparison between shallow and deep anomaly detection techniques is also presented. Our results demonstrate the potential of semi-supervised anomaly detection techniques to extensively explore the present and future hadron colliders’ data.

scholarly journals Auto-Threshold Deep SVDD for Anomaly-based Web Application Firewall

2021 ◽  
Author(s):  
Ali Moradi Vartouni ◽  
Matin Shokri ◽  
Mohammad Teshnehlab
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Web Application ◽  
Threshold Level ◽  
Web Security ◽  
Detection Methods ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Deep Support

Protecting websites and applications from cyber-threats is vital for any organization. A Web application firewall (WAF) prevents attacks to damaging applications. This provides a web security by filtering and monitoring traffic network to protect against attacks. A WAF solution based on the anomaly detection can identify zero-day attacks. Deep learning is the state-of-the-art method that is widely used to detect attacks in the anomaly-based WAF area. Although deep learning has demonstrated excellent results on anomaly detection tasks in web requests, there is trade-off between false-positive and missed-attack rates which is a key problem in WAF systems. On the other hand, anomaly detection methods suffer adjusting threshold-level to distinguish attack and normal traffic. In this paper, first we proposed a model based on Deep Support Vector Data Description (Deep SVDD), then we compare two feature extraction strategies, one-hot and bigram, on the raw requests. Second to overcome threshold challenges, we introduce a novel end-to-end algorithm Auto-Threshold Deep SVDD (ATDSVDD) to determine an appropriate threshold during the learning process. As a result we compare our model with other deep models on CSIC-2010 and ECML/PKDD-2007 datasets. Results show ATDSVDD on bigram feature data have better performance in terms of accuracy and generalization. <br>

scholarly journals Signal Processing for TDM MIMO FMCW Millimeter-Wave Radar Sensors

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xinrong Li ◽  
Xiaodong Wang ◽  
Qing Yang ◽  
Song Fu
Keyword(s):  
Signal Processing ◽  
Millimeter Wave ◽  
Millimeter Wave Radar ◽  
Radar Sensors ◽  
Wave Radar

scholarly journals Small Foreign Object Debris Detection for Millimeter-Wave Radar Based on Power Spectrum Features

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2316
Author(s):  
Peishuang Ni ◽  
Chen Miao ◽  
Hui Tang ◽  
Mengjie Jiang ◽  
Wen Wu
Keyword(s):  
Power Spectrum ◽  
Millimeter Wave ◽  
Pso Algorithm ◽  
Classification Performance ◽  
Support Vector ◽  
Measured Signal ◽  
Foreign Object ◽  
Millimeter Wave Radar ◽  
Ground Clutter ◽  
Wave Radar

Foreign object debris (FOD) detection can be considered a kind of classification that distinguishes the measured signal as either containing FOD targets or only corresponding to ground clutter. In this paper, we propose a support vector domain description (SVDD) classifier with the particle swarm optimization (PSO) algorithm for FOD detection. The echo features of FOD and ground clutter received by the millimeter-wave radar are first extracted in the power spectrum domain as input eigenvectors of the classifier, followed with the parameters optimized by the PSO algorithm, and lastly, a PSO-SVDD classifier is established. However, since only ground clutter samples are utilized to train the SVDD classifier, overfitting inevitably occurs. Thus, a small number of samples with FOD are added in the training stage to further construct a PSO-NSVDD (NSVDD: SVDD with negative examples) classifier to achieve better classification performance. Experimental results based on measured data showed that the proposed methods could not only achieve a good detection performance but also significantly reduce the false alarm rate.

Abnormal Data Detection of Guidance Angle Based On SMP-SVDD For Seeker

2021 ◽  
Author(s):  
Chao Liang ◽  
Xiangrong Zhang ◽  
Dedong Cui ◽  
Zhengang Yan ◽  
Xiangyu Zhang ◽  
...  
Keyword(s):  
Pitch Angle ◽  
Detection Methods ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Detection ◽  
Detection Accuracy ◽  
Angle Data ◽  
Angle Deviation ◽  
Optimal Kernel ◽  
The One

Abstract The accuracy of the pitch angle deviation directly affects the guidance accuracy of the laser seeker. During the guidance process, the abnormal pitch angle deviation data will be produced when the seeker is affected by interference sources. In this paper, aiming to detect abnormal data in seeker pitch angle deviation data, a method based on Smooth Multi-Kernel Polarization Support Vector Data Description (SMP-SVDD) is proposed to detect abnormal data in guidance angle data. On the one hand, the polarization value is used to determine the weight of the multi-kernel combination coefficient to obtain the multi-kernel polarization function, and the particle swarm optimization is used to find the optimal kernel, which improves the detection accuracy. On the other hand, the constrained quadratic programming problem is smooth and differentiable, and the conjugate gradient method can be applied to reduce the complexity of problem solving. Through simulation experiments, it is verified that the SMP-SVDD method has higher detection accuracy and faster calculation speed compared with different detection methods in different guidance stages.

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