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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响/商业潜力是应用低成本射频（RF）敏感性硬件来检测，监视和定位在工业和城市环境中的发射机。该技术可以解决制造和物流运营中的关键问题，例如查找正在干扰操作或监视无线电系统健康的RF发射器。这些活动是在现代工业环境中需要的，因为越来越多的关键系统依赖无线电通信来运行。在城市环境中，自动化检测，监视和本地化发射机的任务可以简化大型无线网络的管理，并在无线设备使用中收集关键数据。增加了故意保障和推出新通信标准（例如5G）的实例，使这些任务对现代城市至关重要。传统上，此类监视任务是通过昂贵的光谱监控设备手动执行的。自动化装置还使用昂贵的传感器，使自动光谱监视仅在机场周围的安全至关重要地区可行。 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 efficiency of existing detection and localization algorithms as applied to the network, and evaluate novel machine-learning (ML) algorithms for similar tasks.该项目还将通过表征商品软件定义的无线电（SDR）硬件可以在各种操作环境中执行的方式来减轻部署此系统的技术风险。为了进行工业和城市频谱监测的目的，没有部署高密度和大规模的测试网络，因此在整个SBIR I期项目中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本地化的ML算法，该项目可以帮助确定在从低成本传感器中摄入RF数据的有效机器学习模型，并综合有关无线电环境的可行输出。该项目的结果将是对各种环境和传感器节点密度之间多种算法的检测和定位性能的分析。 该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估被认为是宝贵的支持。