Reference : Stochastic-Geometry-Based Interference Modeling in Automotive Radars Using Matérn Har...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/45453
Stochastic-Geometry-Based Interference Modeling in Automotive Radars Using Matérn Hard-Core Process
English
Mishra, K. V. [> >]
R., B. Shankar M. [> >]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
4-Dec-2020
2020 IEEE Radar Conference (RadarConf20), Stochastic-Geometry-Based Interference Modeling in Automotive Radars Using Matérn Hard-Core Process
1-5
Yes
Stochastic-Geometry-Based Interference Modeling in Automotive Radars Using Matérn Hard-Core Process
from 21-09-20 to 25-09-20
Florence
Italy
[en] Radar;Interference;Radar cross-sections;Radar antennas;Automotive engineering;Stochastic processes;Spaceborne radar;Automotive radar;interference;Matérn hard-core process;Poisson point process;stochastic geometry
[en] As the use of radars in autonomous driving systems becomes more prevalent, these systems are increasingly susceptible to mutual interference. In this paper, we employ stochastic geometry to model the automotive radar interference in realistic traffic scenarios and then derive trade-offs between the radar design parameters and detection probability. Prior works model the locations of radars in the lane as a homogeneous Poisson point process (PPP). However, the PPP models assume all nodes to be independent, do not account for the lengths of vehicles, and ignore spatial mutual exclusion. In order to provide a more realistic interference effect, we adopt the Matérn hardcore process (MHCP) instead of PPP, in which two vehicles are not closer than an exclusion radius from one another. We show that the MHCP model leads to more practical design trade-offs for adapting the radar parameters than the conventional PPP model.
http://hdl.handle.net/10993/45453
10.1109/RadarConf2043947.2020.9266343
2375-5318

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