A Detection Method of Radar Signal by Wavelet Transforms

2007 ◽  
Author(s):  
Shanwen Zhang ◽  
Jianbo Fan ◽  
Lidan Shou ◽  
Jinxiang Dong
Keyword(s):  
Wavelet Transforms ◽  
Detection Method ◽  
Radar Signal

scholarly journals Fall Detection Using Multiple Bioradars and Convolutional Neural Networks

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5569 ◽  
Author(s):  
Lesya Anishchenko ◽  
Andrey Zhuravlev ◽  
Margarita Chizh
Keyword(s):  
Wavelet Transform ◽  
Input Data ◽  
Detection Method ◽  
Fall Detection ◽  
Modern Medicine ◽  
Radar Signal ◽  
Continuous Wavelet ◽  
Time Frequency ◽  
Frequency Representation ◽  
Life Threatening

A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%.

The Radar Signal Detection Based on Higher Order Statistics

2013 ◽  
Vol 310 ◽  
pp. 421-423
Author(s):  
Chun Yu Wang ◽  
Xing Long Qi ◽  
Run Lan Tian ◽  
Lin Ren
Keyword(s):  
Signal Detection ◽  
Order Statistics ◽  
Gaussian Noise ◽  
Signal Detection Theory ◽  
Detection Method ◽  
Noise Signal ◽  
Higher Order ◽  
Radar Signal ◽  
Higher Order Statistics ◽  
Colored Gaussian Noise

Radar signal detection theory is significant for the radar signal detection, and there are many radar signal detection method at present. In this paper, higher order statistics was used to achieve the radar signal detection. It analyzed the basic theory of higher order statistics and higher order statistics in radar signal detection. And it achieved radar signal detection in the MATLAB software, colored Gaussian noise signal detection method based on dual-spectrum was used to detect the radar signal mixed with man-made noise.

scholarly journals Weak Target Detection Method of Passive Bistatic Radar Based on Probability Histogram

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Panhe Hu ◽  
Qinglong Bao ◽  
Zengping Chen
Keyword(s):  
Target Detection ◽  
Detection Method ◽  
Signal To Noise Ratio ◽  
Scattered Wave ◽  
Detection Methods ◽  
Radar Signal ◽  
Bistatic Radar ◽  
Electronic Countermeasure ◽  
Passive Bistatic Radar ◽  
Weak Target

Passive bistatic radar (PBR) has attracted widespread attention for its capabilities in dealing with the threat of electronic countermeasure, stealth technology, and antiradiation missile. However, passive detection methods are limited by unknown characteristics of the uncooperative illuminators, and conventional radar signal processing algorithms cannot be conducted accurately, especially when the carrier frequency of the transmitting signal is agile and the signal-to-noise ratio (SNR) in the scattered wave of target is low. To address the above problems, this paper presents a novel weak target detection method based on probability histogram, which is then tested by a field experiment. Preliminary results indicate the feasibility of the proposed method in weak target detection.

Optimization and application of LPI radar signal detection method

2014 ◽  
Vol 22 (11) ◽  
pp. 3122-3128
Author(s):  
李娜 LI Na ◽  
王珂 WANG Ke ◽  
李保珠 LI Bao-zhu
Keyword(s):  
Signal Detection ◽  
Detection Method ◽  
Radar Signal

Radar signal compression using wavelet transforms

2001 ◽  
Author(s):  
Yan Zhou ◽  
Fei-peng Li ◽  
Yu Xu ◽  
Qian-qing Qin ◽  
Deren Li
Keyword(s):  
Wavelet Transforms ◽  
Radar Signal ◽  
Signal Compression

scholarly journals Denoising of MST RADAR Signal usingCWT and Overlapping Group Shrinkage

2021 ◽  
Vol 12 (5) ◽  
pp. 1950-1954
Author(s):  
P. Suresh Babu, Et. al.
Keyword(s):  
Wavelet Transforms ◽  
Large Amplitude ◽  
Radar Signal ◽  
Mathematical Tool ◽  
Group Sparsity ◽  
L1 Norm ◽  
Convex Cost ◽  
Mst Radar ◽  
Radar Signals ◽  
Powerful Mathematical Tool

Existing algorithmsare generally denouncing the existence of clusters with large amplitude coefficients. The L1 norm as well as other distinct models of sparsity does not attract a cluster tendency (group sparsity). In the light of a minimisation of convex cost work fusing the blended norm, this work introduces the technique "overlapping group shrinking." The groups are completely overlapping in order to abstain from blocking relics. A basic minimization calculation, in light of progressive replacement, is inferred. A straightforward strategy for setting the regularization boundary, in view of constricting the noise to a predefined level, is portrayed in detail by combining OGS with one of the most powerful mathematical tool wavelet transforms. In fact, the CWT coefficients are processed by OGS to produce a noise-free signal. The CWT coefficients are also processed.The proposed approach is represented on MST RADAR signals, the denoised signals delivered by CWT combined with OGS are liberated from noise.

scholarly journals SAR Target Detection Method Based on Empirical Mode Decomposition

2012 ◽  
Vol 6-7 ◽  
pp. 496-500
Author(s):  
Shi Qi Huang ◽  
Bei He Wang ◽  
Yi Hong Li ◽  
Bei Ge
Keyword(s):  
Signal Processing ◽  
Empirical Mode Decomposition ◽  
Target Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Radar Signal ◽  
Sar Images ◽  
Processing Theory ◽  
Mode Decomposition ◽  
Non Stationary Signal

Empirical mode decomposition (EMD) is a new signal processing theory, and it is very much fitting for non-stationary signal processing, such as radar signal. So this paper proposes the new synthetic aperture radar (SAR) image target detection algorithm after analyzing the characteristics of EMD and SAR images. The proposed method performs the EMD operation, feature extraction, election and fusion, which can reduce the affection of speckle. Experimental results show that the proposed method is very effective.

scholarly journals Denoising of MST RADAR Signal usingCWT and Overlapping Group Shrinkage

2021 ◽  
Vol 12 (5) ◽  
pp. 714-718
Author(s):  
P. Suresh Babu, Dr. G. Sreenivasulu
Keyword(s):  
Wavelet Transforms ◽  
Large Amplitude ◽  
Radar Signal ◽  
Mathematical Tool ◽  
Group Sparsity ◽  
L1 Norm ◽  
Convex Cost ◽  
Mst Radar ◽  
Radar Signals ◽  
Powerful Mathematical Tool

Existing algorithmsare generally denouncing the existence of clusters with large amplitude coefficients. The L1 norm as well as other distinct models of sparsity does not attract a cluster tendency (group sparsity). In the light of a minimisation of convex cost work fusing the blended norm, this work introduces the technique "overlapping group shrinking." The groups are completely overlapping in order to abstain from blocking relics. A basic minimization calculation, in light of progressive replacement, is inferred. A straightforward strategy for setting the regularization boundary, in view of constricting the noise to a predefined level, is portrayed in detail by combining OGS with one of the most powerful mathematical tool wavelet transforms. In fact, the CWT coefficients are processed by OGS to produce a noise-free signal. The CWT coefficients are also processed.The proposed approach is represented on MST RADAR signals, the denoised signals delivered by CWT combined with OGS are liberated from noise.

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