Hybrid acoustic-RF systems offer excellent ranging accuracy, yet they typically come at a power consumption that is too high to meet the energy constraints of mobile IoT nodes. We combine pulse compression and synchronized wake-ups to achieve a ranging solution that limits the active time of the nodes to 1 ms. Hence, an ultra low-power consumption of 9.015 µW for a single measurement is achieved. The operation time is estimated on 8.5 years on a CR2032 coin cell battery at a 1 Hz update rate, which is over 250 times larger than state-of-the-art RF-based positioning systems. Measurements based on a proof-of-concept hardware platform show median distance error values below 10 cm. Both simulations and measurements demonstrate that the accuracy is reduced at low signal-to-noise ratios and when reflections occur. We introduce three methods that enhance the distance measurements at a low extra processing power cost. Hence, we validate in realistic environments that the centimeter accuracy can be obtained within the energy budget of mobile devices and IoT nodes. The proposed hybrid signal ranging system can be extended to perform accurate, low-power indoor positioning.

混合声学RF系统提供出色的测距精度，但通常功耗很高，无法满足移动IoT节点的能源约束。我们将脉冲压缩和同步唤醒结合在一起，以实现将节点的活动时间限制为1 ms的测距解决方案。因此，单次测量可实现9.015 µW的超低功耗。CR2032纽扣电池的工作时间估计为8.5年，更新速率为1 Hz，这比基于RF的最新定位系统大250倍以上。基于概念验证硬件平台的测量结果显示距离误差中值低于10 cm。仿真和测量均表明，在低信噪比下以及发生反射时，精度会降低。我们介绍了三种以较低的额外处理能力成本来增强距离测量的方法。因此，我们验证了在现实环境中可以在移动设备和IoT节点的能量预算范围内获得厘米精度。所提出的混合信号测距系统可以扩展为执行精确的低功率室内定位。