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- W2783133725 abstract "In this work the Doppler effect is exploited for identifying a collision between two vehicles under Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) conditions. A measurement campaign at 5.9GHz is conducted to observe the Doppler effect for Vehicle-to-Vehicle (V2V) networks. In our previous work, we developed a collision avoidance estimator based on tracking the LOS Doppler shift in the Doppler domain. We now report real-world captures of the Doppler phenomenon in terrestrial V2V networks during a collision and present a method for collision identification by tracking the NLOS Doppler spread in the channel. The classification method by Cepstral coefficients is shown to be reliable in NLOS and LOS conditions, allowing for 360-degree coverage of the vehicle using a single omni-antenna. Through machine learning, classification of collision feature observations is accurate with an accuracy of nearly 100% and reliable with a misclassification rate less than 1%." @default.
- W2783133725 created "2018-01-26" @default.
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- W2783133725 date "2017-12-01" @default.
- W2783133725 modified "2023-09-26" @default.
- W2783133725 title "Cepstral Analysis for Classifying Car Collisions in LOS/NLOS Vehicle-to-Vehicle Networks" @default.
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- W2783133725 doi "https://doi.org/10.1109/glocom.2017.8253973" @default.
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