大规模汽车雷达网络的建模与统计特性分析
Modeling and Statistical Characterization of Large-Scale Automotive Radar Networks
AI总结：
本文提出基于线过程和 Cox 过程的街道和点模型，用于分析大规模汽车雷达网络的性能变化及优化设计。
摘要：
离散杂波和同频干扰对汽车雷达网络性能的影响已通过随机几何学进行了研究，特别是通过利用二维泊松点过程（PPPs）。然而，这种表征没有考虑街道几何结构的影响以及汽车雷达的位置被限制在街道上而非整个欧几里得平面。此外，随着车辆驶出市中心向郊区移动，街道结构可能会发生剧烈变化。因此，不仅雷达性能会变化，而且雷达参数和协议也必须进行调整以实现最佳性能。本文提出并表征了基于线过程和 Cox 过程的街道和点模型，以分析大规模汽车雷达网络。我们考虑经典的泊松线过程（PLP）和新引入的二项线过程（BLP）模型来模拟街道，以及相应的基于 PPP 的 Cox 过程来模拟车辆节点。特别是，BLP 模型有效考虑了城市不同区域的街道几何空间变化。我们推导了汽车雷达所经历的有效干扰集，距离干扰源的统计特性，并将自动驾驶雷达的检测概率作为函数来表征街道和车辆密度。最后，利用全球不同城市的城市街道和车辆密度的现实数据，我们展示了雷达性能在城市不同区域以及不同时间的变化情况。因此，我们的研究为网络运营商和汽车制造商提供了必要的系统设计见解，以规划和优化汽车雷达网络。
英文摘要：
The impact of discrete clutter and co-channel interference on the performance of automotive radar networks has been studied using stochastic geometry, in particular, by leveraging two-dimensional Poisson point processes (PPPs). However, such characterization does not take into account the impact of street geometry and the fact that the location of the automotive radars are restricted to the streets as their domain rather than the entire Euclidean plane. In addition, the structure of the streets may change drastically as a vehicle moves out of a city center towards the outskirts. Consequently, not only the radar performance change but also the radar parameters and protocols must be adapted for optimum performance. In this paper, we propose and characterize line and Cox process-based street and point models to analyze large-scale automotive radar networks. We consider the classical Poisson line process (PLP) and the newly introduced Binomial line process (BLP) model to emulate the streets and the corresponding PPP-based Cox process to emulate the vehicular nodes. In particular, the BLP model effectively considers the spatial variation of street geometry across different parts of the city. We derive the effective interference set experienced by an automotive radar, the statistics of distance to interferers, and characterize the detection probability of the ego radar as a function of street and vehicle density. Finally, leveraging the real-world data on urban streets and vehicle density across different cities of the world, we present how the radar performance varies in different parts of the city as well as across different times of the day. Thus, our study equips network operators and automotive manufacturers with essential system design insights to plan and optimize automotive radar networks.
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