Stochastic Geometry Methods for Modelling Automotive Radar Interference

TL;DR

This paper uses stochastic geometry to model and analyze automotive radar interference, providing analytical expressions for interference statistics and proposing a duty cycle design to optimize performance.

Contribution

It introduces a stochastic geometry framework for modeling radar interference with two vehicle distribution models and derives analytical interference and success probability expressions.

Findings

Regularity of vehicle distribution has limited impact on interference statistics

Analytical bounds for worst-case radar performance are derived

A duty cycle design method improves spectrum access performance

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 vehicle spacial distributions, 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. Our study 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 worst case performance. A technique is proposed for designing the duty cycle for random spectrum access which optimizes the total performance. This analytical framework is verified using Monte-Carlo simulations.