SBIR Phase I: Virtual Fences for Sustainable Protection

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力来自于解决世界许多地区的需求，即为自然资源管理者提供可扩展的技术解决方案，以应对人与动物的冲突和快速砍伐森林。自然资源对生态系统的影响很大，有时甚至令人惊讶。甚至单个野生动物和森林保护区的直接经济价值也往往在数亿美元。可持续的与业务相关的保护技术可以作为有效管理的重要工具。拥有总成本低并与传统的人类监测方法有效共存是可持续性的关键。我们寻求将一种可持续的保护系统商业化，因为它提供了低成本，长寿命的设备，易于使用和维护，在不同的自然环境中具有强大的性能，并且可以隐身操作。它还增强了警卫的责任，并使诸如通知管理层和在通信基础设施不容易获得的环境中共享事件的法医证据等任务自动化。拟议的项目解决了降低电池供电，雷达设备为基础的网状网络解决方案的能源消耗的挑战，以持久，时间敏感的自然资源保护。只有在传感器低功耗、基于数据驱动的机器学习的信号处理低功耗和广域网低功耗的情况下，所提出的系统才能实现商业影响。解决所有这些问题将释放商业价值，但影响受到系统中效率最低的部分的限制。目前，雷达的效率是制约系统性能的重要瓶颈；它消耗大约40毫瓦（mW）。将雷达的消耗减少到5mW将会翻倍，而将其减少到1mW将至少增加三倍，电池更换之间的时间。提议的工作将设计、实现和验证雷达子系统的功率降低，并将结果集成到整个系统中。该计划中最大的技术风险是射频子系统，因此将在第一阶段解决。第二阶段将在所有其他雷达子系统中实施功率降低，将结果集成到一个相干传感器中，并将该传感器集成到整个系统中。这种与更大系统的集成还将处理整个系统的优化。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project comes from addressing a demand in many parts of the world for a scalable technological solution for managers of nature resources to deal with human-animal conflicts and rapid deforestation. Natural resources have large and sometimes surprising ecosystem impact. Even the direct economic value of individual wildlife and forest reserves is often in the hundreds of millions of dollars. Operationally relevant protection technologies that are sustainable can serve as an import tool in effective management. Having a low total cost of ownership and effectively co-existing with conventional human surveillance methods is key to sustainability. We seek to commercialize a protection system that is sustainable as it offers low cost, long lived devices, ease of use and maintenance, robust performance across diverse natural environments, and stealthy operation. It also augments accountability of the guards, and automates tasks such as notifying management and sharing forensic evidence of incidents in settings where communication infrastructure is not readily available.The proposed project addresses the challenge with reducing energy consumption for our battery powered, radar device based, mesh network solution for persistent, time-sensitive protection of natural resources. Commercial impact for the proposed system can only be realized if the sensor is low-power, the data driven machine learning based signal processing is low-power, and the wide area networking is low-power. Addressing all these will unlock commercial value, but the impact is limited by the least efficient part of the system. Today the radar's efficiency is the essential bottleneck in system performance; it consumes about 40 milliWatts (mW). Reducing the radar's consumption to 5mW would double, and reducing it to 1mW would at least triple, the time between battery changes. The proposed effort would design, implement, and validate power reduction in the radar subsystem and integrate the result into an overall system. The largest technical risk in the plan is the Radio Frequency subsystem, which will therefore be addressed in Phase I. Phase II will implement power reductions in all the other radar subsystems, integrate the results into a coherent sensor, and integrate that sensor into the overall system. This integration into the larger system will also deal with whole system optimizations.