Preliminary results from beehive activity monitoring using a 77 GHz FMCW radar sensor

Author(s):  
Luca Dall'Asta ◽  
Georg Egger
Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 573 ◽  
Author(s):  
Onur Toker ◽  
Suleiman Alsweiss

In this paper, we propose a novel 77 GHz automotive radar sensor, and demonstrate its cyberattack resilience using real measurements. The proposed system is built upon a standard Frequency Modulated Continuous Wave (FMCW) radar RF-front end, and the novelty is in the DSP algorithm used at the firmware level. All attack scenarios are based on real radar signals generated by Texas Instruments AWR series 77 GHz radars, and all measurements are done using the same radar family. For sensor networks, including interconnected autonomous vehicles sharing radar measurements, cyberattacks at the network/communication layer is a known critical problem, and has been addressed by several different researchers. What is addressed in this paper is cyberattacks at the physical layer, that is, adversarial agents generating 77 GHz electromagnetic waves which may cause a false target detection, false distance/velocity estimation, or not detecting an existing target. The main algorithm proposed in this paper is not a predictive filtering based cyberattack detection scheme where an “unusual” difference between measured and predicted values triggers an alarm. The core idea is based on a kind of physical challenge-response authentication, and its integration into the radar DSP firmware.


Author(s):  
A. Haderer ◽  
R. Feger ◽  
J. Schrattenecker ◽  
C. Wagner ◽  
A. Stelzer
Keyword(s):  

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 6746-6757 ◽  
Author(s):  
Oh-Yun Kwon ◽  
Chenglin Cui ◽  
Jun-Seong Kim ◽  
Jae-Hyun Park ◽  
Reem Song ◽  
...  

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2001 ◽  
Author(s):  
Eugin Hyun ◽  
YoungSeok Jin

In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.


2016 ◽  
Vol 26 (7) ◽  
pp. 540-542 ◽  
Author(s):  
Jae-Hoon Song ◽  
Chenglin Cui ◽  
Seong-Kyun Kim ◽  
Byung-Sung Kim ◽  
Sangwook Nam

2018 ◽  
Vol 18 (8) ◽  
pp. 3278-3289 ◽  
Author(s):  
Zhenyuan Zhang ◽  
Zengshan Tian ◽  
Mu Zhou

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