Recent years have seen rapid advancements in ultra-wideband (UWB)-based localization systems. However, most existing solutions offer only centimeter-level accuracy and support a limited number of UWB tags, which fails to meet the growing demands of emerging sensing applications (e.g., virtual reality). This paper presents MULoc, the first system that can localize an unlimited number of UWB tags with millimeter-level accuracy. At the core of MULoc is the innovative use of UWB phase, which can provide finer-grained distance measurement than traditional time-of-f1ight (ToF) estimates. To accurately obtain phase estimates from unsynchronized devices, we introduce a novel localization scheme called anchor overhearing (AO) and eliminate raw signal errors through a signal-difference-based technique. For precise tag localization, we resolve phase ambiguity by combining a fusion-based filtering method and frequency hopping. We implement MULoc on commercial UWB modules. Extensive experiments demonstrate that our system achieves a median localization error of 0.47 mm and 90-th percentile error of 1.02 cm, reducing the error of traditional method by 91.12%.

Beyond communication, wireless signals have been extensively utilized for localization, tracking, and sensing in recent years. The key information extracted for these purposes includes distance and angle. While distance measurement accuracy is mainly limited by signal bandwidth, angle accuracy depends on the number of antennas and phase noise. Conventional approaches typically improve angle estimation by boosting signal strength and increasing the number of antennas. In this paper, we propose employing a quantum receiver to substantially improve angle estimation performance. Rather than amplifying signal strength, the quantum receiver reduces the inherent hardware noise. Furthermore, we exploit the unique properties of a quantum RF receiver to construct a multi-antenna quantum system. Using only two physical quantum antennas, we generate virtual antennas by leveraging the receiver's broad frequency range, effectively increasing the number of antennas and significantly improving angle measurement performance. Our experimental results demonstrate that, with only two quantum antennas, we achieve angle estimation performance surpassing that of a conventional RF receiver equipped with 40 antennas. Furthermore, quantum antennas are not constrained by the coupling effects that typically limit the spacing between conventional RF antennas, allowing for much closer placement. This represents a significant step toward reducing the size of antenna arrays while preserving localization and tracking performance.

Besides the communication function, various RF signals such as WiFi and RFID have been actively exploited for sensing purposes recently. However, a missing component of existing RF sensing is sensing under device motions. This paper takes the first step to involve device mobility into the ecosystem of RF sensing. Owning to the miniaturization and low cost of ultra-wideband (UWB) chips in recent years, we propose to integrate the accuracy of UWB sensing with device mobility to support truly ubiquitous RF sensing. This is a challenging task because the motion artifacts from RF devices can easily overwhelm the target motion, such as subtle chest displacement for respiration sensing. In this demo, we propose Mobi2Sense to support sensing under device motions. We propose novel signal processing schemes to remove the effect of device motions on sensing and prototype Mobi2Sense using a commodity UWB module. Comprehensive evaluation demonstrates that Mobi2Sense is able to "hear" music and "see" human respiration at high accuracy in the presence of device motions.

Among various wireless sensing modalities, Ultra-Wideband (UWB) exhibits unique advantages such as fine granularity owing to its super large bandwidth (500 MHz - 2 GHz). Though promising, UWB sensing was only demonstrated on dedicated hardware including DW1000 and XETHRU X4 which are not available in existing consumer-level devices. In the last few years, we observed an interesting trend of UWB module being embedded into consumer-level devices such as smartphones and smart watches. However, leveraging UWB module inside consumer-level devices for sensing poses new challenges. One key challenge is that while dedicated UWB hardware can present us with raw physical-layer signal amplitude and phase, only upper-layer distance and angle information can be extracted from consumer-level devices. In this demo, we address the challenges and present the first UWB sensing system hosted on iPhone and Apple Watch without any dedicated hardware components. We show that with just the upper-layer UWB data reported from smartphones, exciting sensing applications such as fine-grained 3D handwriting and multi-target tracking can be realized, pushing RF sensing one step forward towards real-life adoption.

While GPS-based outdoor localization has become a norm, very few indoor localization systems have been deployed and used. In this paper, we share our 5-year experience on the design, development and evaluation of a large-scale WiFi indoor localization system. We address practical challenges encountered to bridge the gap between indoor localization research in the laboratory and system deployment in the wild. The system is currently used in 1469 shopping malls, 393 office buildings and 35 hospitals across 35 cities to provide location service to millions of users on a daily basis. We hope the shared experience can benefit the design of real-world indoor localization systems and the practical problems identified can change the focus of indoor localization research. We released our dataset that contains fingerprints collected from 1469 shopping malls and one office building.

Besides the conventional communication function, wireless signals are actively exploited for sensing purposes recently. However, a missing component of existing wireless sensing is sensing under device motions. This is challenging because device motions can easily overwhelm target motions such as chest displacement used for respiration sensing. This paper takes a first step in the direction of involving device mobility into the ecosystem of wireless sensing. Owning to the miniaturization and low cost of ultra-wideband (UWB) chip in recent years, we propose to integrate the accuracy of UWB sensing with mobility to support truly ubiquitous wireless sensing. We propose Mobi2Sense, a system design to support sensing under device motions. We propose novel signal processing schemes to remove the effect of device motions on sensing and prototype Mobi2Sense using commodity UWB hardware. Real-world applications demonstrate that even in the presence of device motions, fine-grained Mobi2Sense is able to capture subtle target motions to "hear" music, "see" human respiration, and "recognize" multi-target gestures at a high accuracy.