Stochastic geometry methods for modeling automotive radar interference

Hourani, A, Evans, R, Sithamparanathan, K, Moran, W and Eltom, H 2017, 'Stochastic geometry methods for modeling automotive radar interference', IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 2, pp. 333-344.

Document type: Journal Article
Collection: Journal Articles

Title Stochastic geometry methods for modeling automotive radar interference
Author(s) Hourani, A
Evans, R
Sithamparanathan, K
Moran, W
Eltom, H
Year 2017
Journal name IEEE Transactions on Intelligent Transportation Systems
Volume number 19
Issue number 2
Start page 333
End page 344
Total pages 12
Publisher IEEE
Abstract As the use of automotive radar increases, performance limitations associated with radar-to-radar interference will become more significant. In this paper, we employ tools from stochastic geometry to characterize the statistics of radar interference. Specifically, using two different models for the spatial distributions of vehicles, namely, a Poisson point process and a Bernoulli lattice process, we calculate for each case the interference statistics and obtain analytical expressions for the probability of successful range estimation. This paper shows that the regularity of the geometrical model appears to have limited effect on the interference statistics, and so it is possible to obtain tractable tight bounds for the worst case performance. A technique is proposed for designing the duty cycle for the random spectrum access, which optimizes the total performance. This analytical framework is verified using Monte Carlo simulations.
Subject Electrical and Electronic Engineering not elsewhere classified
Signal Processing
Wireless Communications
Keyword(s) Automotive radars
stochastic geometry
interference modeling
radar performance estimation
ranging success probability
radar performance optimization
DOI - identifier 10.1109/TITS.2016.2632309
Copyright notice © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
ISSN 1524-9050
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Citation counts: TR Web of Science Citation Count  Cited 16 times in Thomson Reuters Web of Science Article | Citations
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