SBIR Phase I: Ultra-Low-Cost Distributed Spectrum Monitoring

SBIR 第一阶段：超低成本分布式频谱监测

• 批准号: 2112062
• 负责人: Isaac Struhl
• 金额: $ 23.19万
• 依托单位: DISTRIBUTED SPECTRUM LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2112062&HistoricalAwards=false
• 关键词: SBIR; Phase; Ultra; Low; Cost

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to apply low-cost radiofrequency (RF) sensing hardware to detect, monitor, and localize transmitters in industrial and urban environments. This technology can solve key problems in manufacturing and logistics operations such as finding RF transmitters that are interfering with operations or monitoring the health of radio systems. These activities are required in modern industrial environments as increasing numbers of critical systems rely on radio communications to operate. In urban environments, automating the tasks of detecting, monitoring, and localizing transmitters can simplify the management of large-scale radio networks and gather critical data on wireless device usage. Increased instances of intentional jamming and the rollout of new communications standards such as 5G make these tasks critical to the modern city. Traditionally, such monitoring tasks are conducted manually with expensive spectrum monitoring equipment. Automated installations also utilize expensive sensors, making automated spectrum monitoring only feasible in safety-critical areas such as around airports. Lower-cost sensors can be installed permanently at a high density for almost any application, allowing cities and smaller industrial customers to deploy persistent monitoring networks.This Small Business Innovation Research (SBIR) Phase I project seeks to deploy high-density networks of low-cost sensors, determine the efficacy of existing detection and localization algorithms as applied to the network, and evaluate novel machine-learning (ML) algorithms for similar tasks. This project will also mitigate the technical risk of deploying such a system by characterizing how well commodity software-defined radio (SDR) hardware can perform across a variety of operational environments. No high-density and large-scale test networks of inexpensive radio hardware have been deployed for the purposes of industrial and urban spectrum monitoring, so data gathered throughout this SBIR Phase I project will be useful in evaluating the viability of this approach. By evaluating ML algorithms for signal detection and localization, this project can help determine how effective machine learning models can be at ingesting RF data from low-cost sensors and synthesizing actionable outputs about the radio environment. The result of this project will be an analysis of the detection and localization performance of a variety of algorithms across different environments and sensor node densities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该小型企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力是应用低成本射频 (RF) 传感硬件来检测、监控和定位工业和城市环境中的发射机。该技术可以解决关键问题。在现代工业环境中，需要进行这些活动，因为越来越多的关键系统依赖于无线电通信来运行。检测任务，监控和定位发射机可以简化大规模无线电网络的管理并收集有关无线设备使用情况的关键数据。传统上，故意干扰的情况增加以及 5G 等新通信标准的推出使得这些任务变得至关重要。此类监测任务是通过昂贵的频谱监测设备手动执行的，自动化安装也使用昂贵的传感器，使得自动化频谱监测仅在机场周围等安全关键区域可行。应用程序，允许城市和较小的工业客户部署持续监控网络。这个小型企业创新研究 (SBIR) 第一阶段项目旨在部署低成本传感器的高密度网络，确定应用于网络的现有检测和定位算法的功效，并评估新颖的网络该项目还将通过表征商用软件定义无线电（SDR）硬件在各种高密度操作环境中的执行情况来降低部署此类系统的技术风险。以及廉价无线电的大规模测试网络硬件已用于工业和城市频谱监测，因此整个 SBIR 第一阶段项目收集的数据将有助于评估这种方法的可行性。通过评估用于信号检测和定位的 ML 算法，该项目可以帮助确定如何实现。有效的机器学习模型可以从低成本传感器获取射频数据并合成有关无线电环境的可操作输出，该项目的结果将是对不同环境和传感器节点的各种算法的检测和定位性能进行分析。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。