Weak radar signal detection based on wavelet transform

1994 ◽  
Vol 77 (8) ◽  
pp. 105-114
Author(s):  
Naoki Ehara ◽  
Iwao Sasase ◽  
Shinsaku Mori
Keyword(s):  
Wavelet Transform ◽  
Signal Detection ◽  
Radar Signal

Sampling losses in radar signal detection

1986 ◽  
Vol 56 (6-7) ◽  
pp. 237 ◽  
Author(s):  
D. D'Aloisi ◽  
A. Di Vito ◽  
G. Galati
Keyword(s):  
Signal Detection ◽  
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%.

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