Separation of Incident and Reflected Waves by Means of a Wave Radar System

Author(s):  
Francesco Serafino ◽  
Simone Bonamano ◽  
Francesco Paladini de Mendoza ◽  
Marco Marcelli
Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5228
Author(s):  
Jin-Cheol Kim ◽  
Hwi-Gu Jeong ◽  
Seongwook Lee

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.


2019 ◽  
Vol 2019 ◽  
pp. 1-6
Author(s):  
Shang Shang ◽  
Kangning He ◽  
Zhaobin Wang ◽  
Xuguang Yang

In HFSWR (high-frequency surface-wave radar) system, the detection performance is impacted seriously by ionospheric clutter. Frequency selection is an effective method to avoid the effect of ionospheric clutter. The key to the method is the stationarity of ionospheric clutter over a period of time. This paper mainly researches the stationary time statistical property of the ionospheric clutter. A large number of real data including ionospheric clutter in HFSWR are processed and analyzed. It shows that ionospheric clutter in HFSWR has the characteristics of approximate stationarity within a period of time.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sven Schellenberger ◽  
Kilin Shi ◽  
Tobias Steigleder ◽  
Anke Malessa ◽  
Fabian Michler ◽  
...  

Abstract Using Radar it is possible to measure vital signs through clothing or a mattress from the distance. This allows for a very comfortable way of continuous monitoring in hospitals or home environments. The dataset presented in this article consists of 24 h of synchronised data from a radar and a reference device. The implemented continuous wave radar system is based on the Six-Port technology and operates at 24 GHz in the ISM band. The reference device simultaneously measures electrocardiogram, impedance cardiogram and non-invasive continuous blood pressure. 30 healthy subjects were measured by physicians according to a predefined protocol. The radar was focused on the chest while the subjects were lying on a tilt table wired to the reference monitoring device. In this manner five scenarios were conducted, the majority of them aimed to trigger hemodynamics and the autonomic nervous system of the subjects. Using the database, algorithms for respiratory or cardiovascular analysis can be developed and a better understanding of the characteristics of the radar-recorded vital signs can be gained.


2013 ◽  
Vol E96.B (9) ◽  
pp. 2313-2322 ◽  
Author(s):  
Takaaki KISHIGAMI ◽  
Tadashi MORITA ◽  
Hirohito MUKAI ◽  
Maiko OTANI ◽  
Yoichi NAKAGAWA

2013 ◽  
Vol 329 ◽  
pp. 338-343
Author(s):  
Tian Jiao Fu ◽  
Li Guo Zhang ◽  
Jian Yue Ren

The azimuthal measurements of the high frequency ground wave radar are poor in an actual environment, which can cause the plots highly decentralized and damage the formation of the over-the-horizon tracks. To solve the problem, a new radar system is proposed to triangulate target tracks using range and Doppler measurements only. On the basis of the analysis of the characteristics of the range-finding location, a multi-target tracking algorithm under non-clutter condition is given in this paper, which further improves the tracking algorithm of this system. Simulation results show the effectiveness of this method.


Sign in / Sign up

Export Citation Format

Share Document