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

这个小企业创新研究（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。这项技术可以解决制造和物流操作中的关键问题，例如发现干扰操作的射频发射机或监测无线电系统的健康状况。这些活动在现代工业环境中是必需的，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、监控和定位发射机的任务可以简化大规模无线网络的管理，并收集无线设备使用的关键数据。越来越多的故意干扰事件和5G等新通信标准的推出，使得这些任务对现代城市至关重要。传统上，这种监测任务是用昂贵的频谱监测设备手动进行的。自动化装置还使用昂贵的传感器，使得自动化频谱监测仅在机场周围等安全关键区域可行。低成本的传感器可以在几乎任何应用中以高密度永久安装，允许城市和小型工业客户部署持久的监控网络。这个小企业创新研究（SBIR）第一阶段项目旨在部署高密度的低成本传感器网络，确定应用于网络的现有检测和定位算法的有效性，并评估用于类似任务的新型机器学习（ML）算法。该项目还将通过描述商品软件定义无线电（SDR）硬件在各种操作环境中的性能，降低部署这种系统的技术风险。为了工业和城市频谱监测的目的，还没有部署高密度和大规模的廉价无线电硬件测试网络，因此在整个SBIR第一阶段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定位的ML算法，该项目可以帮助确定机器学习模型在从低成本传感器获取射频数据和合成有关无线电环境的可操作输出方面的有效性。该项目的结果将是分析各种算法在不同环境和传感器节点密度下的检测和定位性能。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.