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.

The digital transformation is exciting the uptake of Internet-of-Things technologies, and raises the questions surrounding our knowledge of the positions of many of these things. A review of indoor localization technologies summarized in this paper shows that with conventional RF-based techniques, a significant challenge exists in terms of achieving good accuracy with a low power consumption at the device side. We present hybrid RF-acoustic approaches as an interesting alternative: the slow propagation speed of sound allows for accurate distance measurements, while RF can easily provide synchronization, data, and power to the devices. We explain how the combination of adequate signaling realizing a late wake-up of the devices with backscattering could position energy-neutral devices. Experiments in a real-life testbed confirmed the potential 10 cm-accuracy based on RF-harvested energy. Nonetheless, these also expose open challenges to be resolved in order to achieve accurate 3D positioning.

Wireless power transfer (WPT) is a promising service for the Internet of Things (IoT), providing a cost-effective and sustainable solution to deploy so-called energy-neutral devices on a massive scale. The power received at the device side from a conventional transmit antenna with a physically small aperture decays rapidly with the distance. New opportunities arise from the transition from conventional far-field beamforming to near-field beam focusing. We argue that a physically large aperture, that is large with respect to the distance to the receiver, enables a power budget that remains practically independent of distance. Distance-dependent array gain patterns allow focusing the power density maximum precisely at the device location, while reducing the power density near the infrastructure. Physical aperture size is a key resource in enabling efficient yet regulatory-compliant WPT. We use real-world measurements to demonstrate that a regulatory-compliant system operating at sub-10 GHz frequencies can increase the power received at the device into the milliwatt range. Our empirical demonstration shows that power-optimal near-field beam focusing inherently exploits multipath propagation, yielding both increased WPT efficiency and improved human exposure safety.

With increasing interest in indoor positioning systems across various domains, such as industry, retail, and healthcare, the search for optimal solutions to meet the needs of different applications has gained significant momentum. This work highlights the potential of hybrid RF-acoustic systems combined with advanced machine learning models for robust, scalable, and energy-efficient indoor localization. The focus is on enhancing positioning algorithms for energy-neutral devices to improve accuracy, precision, reliability, and ease of installation. Traditional model-based (MB) methods, relying on line-of-sight (LoS) components, often struggle in challenging nonline-of-sight (NLoS) and reverberant environments. To address this, we propose data-driven neural network (NN) approaches capable of harnessing multipath components (MPCs) as additional information. The echoes in the room are exploited to improve accuracy. Various NN architectures, including multilayer perceptrons, (circular) convolutional neural networks, and graph neural networks (GNNs) are evaluated, in first instance using synthetic data. Results demonstrate that especially GNNs outperform MB methods, achieving superior accuracy in both LoS and NLoS scenarios. During the second phase, extensive real-life experiments are carried out. The GNN is evaluated using cross-validation, training on measurement data, and transfer learning (TL) within a reverberant NLoS environment. The cross-validation and TL demonstrate the practical feasibility. We report over 80% of improvement in 3-D positioning error compared to the MB technique.

Sled-based side-looking C-band radar profiles were collected around Fairbanks, Alaska, in March 2023 during the NASA SnowEx campaign to improve the conceptual understanding of C-band radar wave interactions with snow in a boreal forest environment. Seven transects with different vegetation and ground conditions were studied. Significant volume scattering from snow was observed in this shallow snowpack, indicating sensitivity at lower snow depths which are common in high-latitude snowpacks. Manual removal of the snowpack decreased the backscatter by more than 2 dB in all polarizations, with a larger decrease in the cross-polarization, supporting the potential use of Sentinel-1 to retrieve snow depth.

In this letter we zoom in on the distance estimation in a chirp-based hybrid RF-acoustic ranging system. We present an ultra low-power solution based on zero-crossing demodulation. We have designed an experimental prototype to validate the method and compared the power usage, accuracy and precision of this ranging system with a conventional method. Our measurements show that an accuracy and precision below 5 cm can be obtained for a mobile sensor node that has a lifespan of over 3794 days or ~10 years, at an update rate of 1 Hz. This constitutes a 4 times improvement with respect to conventional ADC-based indoor ranging techniques. The provided method can be extended to a system for indoor localisation and also opens the possibility to implement RF backscattering-based solutions for fully passive nodes.