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. Melbourne. He was a Radio Network Planning Engineer for mobile telecom industry, and then as an ICT Program/Engineering Manager for several projects spanning over different technologies, including mobile networks deployment, satellite networks, and railway ICT systems for seven years. He is currently a Lecturer with the School of Engineering, RMIT University. His current research interests include the application of stochastic geometry, cognitive automotive radars, radar signal processing, and aerial communication systems. He received the Australian Post Graduate Award, funded by the Australian government, from RMIT University.
In REF , the performance of automotive radar is evaluated in terms of expected signal-to-noise ratio, when the locations of vehicles follow a Poisson point process and a Bernoulli lattice process.
Stochastic Geometry Methods for Modeling Automotive Radar Interference
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Mathematics Computer Science Engineering