Chaudhary Sushank, Khichar Sunita, Meng Yahui, Sharma Abhishek
School of Computer, Guangdong University of Petrochemical Technology, Maoming, China.
Department of Electrical Engineering, Chulalongkorn University, Bangkok, Thailand.
PLoS One. 2025 May 13;20(5):e0322693. doi: 10.1371/journal.pone.0322693. eCollection 2025.
Photonic radar systems offer a promising solution for high-precision sensing in various applications, particularly in autonomous vehicles, where reliable detection of obstacles in real-time is critical for safety. However, environmental conditions such as atmospheric turbulence and rain attenuation significantly impact radar performance, potentially compromising detection accuracy. This study aims to assess the performance of a photonic radar system under different environmental scenarios, including free-space, Gamma-Gamma atmospheric turbulence, and light and heavy rain conditions, with a focus on detecting three distinct targets positioned at various distances. Our simulations demonstrate that Gamma-Gamma atmospheric turbulence introduces variability in the received signal, with fluctuations becoming more pronounced at greater distances. Additionally, rain attenuation was found to substantially degrade performance, with heavy rain causing up to a 1 dBm reduction in received power at 50 meters and nearly a 1.5 dBm reduction at 100 meters, compared to light rain. For three targets located at 50m, 100m, and 150m, the combined effects of rain and turbulence were particularly noticeable at longer distances, with the received power under heavy rain dropping to -100.4 dBm at 150 meters. These findings indicate the importance of accounting for environmental conditions in the design of photonic radar systems, especially for autonomous vehicle applications. Future improvements could focus on developing adaptive radar techniques to compensate for adverse weather effects, ensuring robust and reliable performance under varying operational conditions. The novelty of this study lies in the integration of photonic radar technology with an advanced modeling framework that accounts for both free-space propagation and adverse weather conditions. Unlike conventional radar studies, our work incorporates Gamma-Gamma turbulence modeling and rain attenuation effects to provide a more comprehensive analysis of radar performance in real-world environments. This study also proposes an optimized detection strategy for multiple targets at varying distances, demonstrating the potential of photonic radar for autonomous vehicle applications.
光子雷达系统为各种应用中的高精度传感提供了一个有前景的解决方案，特别是在自动驾驶车辆中，实时可靠地检测障碍物对安全至关重要。然而，诸如大气湍流和降雨衰减等环境条件会显著影响雷达性能，可能会损害检测精度。本研究旨在评估光子雷达系统在不同环境场景下的性能，包括自由空间、伽马-伽马大气湍流以及小雨和大雨条件，重点是检测位于不同距离的三个不同目标。我们的模拟表明，伽马-伽马大气湍流会使接收信号产生变化，在更远的距离波动会变得更加明显。此外，发现降雨衰减会大幅降低性能，与小雨相比，大雨在50米处会导致接收功率降低多达1 dBm，在100米处降低近1.5 dBm。对于位于50米、100米和150米处的三个目标，降雨和湍流的综合影响在更远的距离尤为明显，在150米处大雨下的接收功率降至-100.4 dBm。这些发现表明在光子雷达系统设计中考虑环境条件的重要性，特别是对于自动驾驶车辆应用。未来的改进可以集中在开发自适应雷达技术以补偿不利天气影响，确保在不同运行条件下具有稳健可靠的性能。本研究的新颖之处在于将光子雷达技术与一个先进的建模框架相结合，该框架考虑了自由空间传播和不利天气条件。与传统雷达研究不同，我们的工作纳入了伽马-伽马湍流建模和降雨衰减效应，以便对现实环境中的雷达性能进行更全面的分析。本研究还提出了一种针对不同距离的多个目标的优化检测策略，展示了光子雷达在自动驾驶车辆应用中的潜力。
