The rapid growth of automotive radar technology necessitates new statistical models that account for the variation of vehicular density on roads with time, with different areas within cities and between cities across the globe for better sensor design and road planning. Stochastic geometry has emerged as a valuable analytical framework for modeling large-scale automotive radar networks in dense urban environments where interfering radars and discrete clutter scatterers are modeled as point processes. Here, each road environment encountered by a radar is treated not as an independent setting but as an instance of an underlying spatial stochastic process. By suitably modifying parameters, such as the vehicular density on roads or the street density in a region, this process can be adapted to diverse road conditions across geography and time. The resulting analyses provide valuable system-level insights at a fraction of the cost of comparable Monte Carlo simulations. In particular, the framework enables the derivation of the statistics of the radar detection probability and, consequently, the optimization of the parameters to maximize this probability. This article briefly reviews the nascent state-of-the-art research on modeling automotive radar networks and identifies several promising future research directions and challenges.
Emerging Trends in Radar: Automotive Radar Networks
IEEE Aerospace and Electronic Systems Magazine ; 40 , 6 ; 54-59
2025-06-01
730384 byte
Article (Journal)
Electronic Resource
English
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