2024/11/27 by Gourab Ghatak, Ghatak, Gourab
Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Information Theory (cs.IT) #Radar Systems and Signal Processing
paper · pdf · doi:10.48550/arxiv.2411.18252
openalex publication_date 2024/11/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We introduce a novel metric for stochastic geometry based analysis of automotive radar networks called target \it tracking probability. Unlike the well-investigated detection probability (often termed as the success or coverage probability in stochastic geometry), the tracking probability characterizes the event of successive successful target detection with a sequence of radar pulses. From a theoretical standpoint, this work adds to the rich repertoire of statistical metrics in stochastic geometry-based wireless network analysis. To optimize the target tracking probability in high interference scenarios, we study a block medium access control (MAC) protocol for the automotive radars to share a common channel and recommend the optimal MAC parameter for a given vehicle and street density. Importantly, we show that the optimal MAC parameter that maximizes the detection probability may not be the one that maximizes the tracking probability. Our research reveals how the tracking event can be naturally mapped to the quality of service (QoS) requirements of latency and reliability for different vehicular technology use-cases. This can enable use-case specific adaptive selection of radar parameters for optimal target tracking.