High-accuracy indoor positioning systems provide various location-aware applications, enhancing our daily experiences. The Global Navigation Satellite System is difficult to use indoors, leading to the development of various indoor positioning methods. Among these, acoustic fingerprint-based positioning, which utilizes widely available speakers and microphones, provides robust performance in Non-Line-of-Sight (NLOS) environments that are common due to structural obstacles and furniture. These approaches typically use Received Signal Strength Indicator or Power Spectral Density as location fingerprints but face challenges in achieving high positioning accuracy. In this paper, we propose Geometric Sound Profile (GSP), a temporal feature of complex reflection waves, enabling high-accuracy localization with a single speaker. GSP, defined as the envelope of cross-correlation between the transmitted and received signals, starting from the transmission time, serves as a highly informative feature encapsulating the multipath-Time-of-Flight. Additionally, we implement a Convolutional Neural Network for the estimation of the user’s position using GSP. We generated a high-resolution pre-trained model in the simulation and fine-tuned it with measurement data, allowing accurate positioning with minimal measured training data. Our experiments demonstrated that the median positioning error was 0.14 m and the 90th percentile error was 1.23 m in the Line-of-Sight environment, and the median positioning error was 0.09 m and the 90th percentile error was 1.14 m in the NLOS environment.

We present a visible light positioning (VLP) method that allows indoor positioning using a smartphone camera with a narrow field of view (FoV). Our method employs omni-directional LEDs, each modulated at a unique frequency, mounted on the ceiling. The system calculates the 2-D position and azimuth of the smartphone by analyzing the brightness of ceiling reflection captured by the user’s smartphone front camera. This approach does not necessitate a direct line-of-sight to the LEDs, effectively overcomes the FoV limitations, and enables positioning over a wider space. We model the ceiling reflection as a transmission path between the LEDs and the ceiling, improving coverage and accuracy of estimation for the user’s position and azimuth compared to previous work. In experiments with two LEDs placed 2.6 m apart, our method covered a space over ten times larger than traditional methods, achieving coverage of $50 \mathbf{m}^{2}$ with a mean absolute error (MAE) in the positioning of 0.46 m and an MAE in azimuth estimation of 13.0°.

Low-cost and accurate indoor location information can add spatiotemporal context to information systems, enabling new location-aware applications. Time-of-Flight (ToF)-based acoustic localization using speakers and microphones allows for localization accuracy within a few tens of centimeters, outperforming RF-based techniques. However, ToF-based localization requires synchronization between the speaker and microphone, i.e., the time offset between them must be known. Previous time offset estimation methods required custom hardware for speakers, limiting their practical use. Estimating the time offset using a single, unmodified speaker is essential for leveraging widely deployed speakers and enhancing coverage. This paper presents the first method for time offset estimation using a single speaker and a microphone, enabled by two key factors: (i) a time offset computation method that utilizes higher-order floor-ceiling reflections as multiple geometrically-constrained virtual speakers, and (ii) a signal processing pipeline that isolates these critical reflections from numerous others by leveraging the speaker's frequency-dependent radiation pattern. Experiments show that the proposed technique can achieve time offset estimation with a 90th percentile error of 259 μs at a 5 m distance. Furthermore, we implemented a ToF localization system based on SyncEcho, demonstrating a 11.0 cm localization accuracy with a 90th percentile error.

This paper investigates a novel millimeter wave (mmWave) radar-based inclination estimation to enhance the landing capabilities of unmanned aerial vehicles (UAVs) in poor visibility conditions. Unlike SAR based approaches, this method facilitates one-shot estimation by using known reflector patterns on landing surfaces. As a first step, we evaluate the performance of the estimation with obtained distance information, assuming that the coordinates of the UAV and port are known. The experimental results demonstrate that mmWave radar with centimeter-ordered resolution can estimate the inclination of less than 5° errors.

This paper presents MilliSign, a guidance system based on a batteryless tag to support unmanned aerial vehicles in all-weather conditions. Conventional batteryless guidance systems using visual signs fail to work in inclement weather due to poor visibility. The need for all-weather operation with long-range readability encourages the use of millimeter wave (mmWave) radar, which poses challenges in providing a wide 3-D read range and low-cost operation. To address these challenges, we introduce a corner reflector (CR) array-based chipless RFID tag and a one-shot slant range reading procedure with COTS mmWave radar. We establish a novel design method for the shape and alignment of CR units to decrease the tag's size and expand the 3-D read range. Additionally, we develop a signal-processing pipeline based on Root-MUSIC to achieve accurate power and spatial estimation, which facilitate automatic tag detection. Our evaluation demonstrates that the tag, measuring 292 mm × 600 mm × 19 mm and storing 8 bits, can be read by mmWave radar from a distance of more than 10 m with a viewing angle of more than 30° in elevation and azimuth. Moreover, its performance remains stable in poor visibility conditions and multipath-rich environments.

Robust indoor positioning systems provide stable location-aware applications, enhancing our daily experiences. Fingerprint-based positioning techniques enable estimation of a user's position in complex indoor environments. While previous studies have used the received signal strength indicator or power spectral density as fingerprints, they typically achieved only submeter accuracy. This paper presents Geometric Sound Profile (GSP) as a novel location fingerprint to elevate the performance ceiling of fingerprint-based positioning. GSP is derived from the cross-correlation of transmitted and received signals based on transmission time, and a user's position is computed using weighted k-nearest neighbors. Our experiments demonstrate a median error of 0.66 m, marking a significant advancement over previous fingerprinting techniques.

The advancement of semiconductor and battery technologies popularized tiny acoustic wearable devices such as bone conduction wireless headsets. However, this small form factor poses inconvenience when controlling these devices, as they cannot equip large footprint intuitive interfaces such as volume sliders and touch screens. This paper presents a technique using acoustic responses measured by a bone conduction speaker and a microphone to utilize the ear as a touch input interface. We discovered that a finger placed on different parts of the ear affects the acoustic radiation characteristic of the ear, modulating the leaked sound, and by leveraging this effect, the touch position can be estimated. Experimental results show that five distinct frequency responses with five different finger positions can be obtained, which indicates that our method could allow bone conduction headsets to capture continuous finger positions without additional hardware.

TelemetRing is a batteryless and wireless ring-shaped keyboard that supports command and text entry in daily lives by detecting finger typing on various surfaces. The proposed inductive telemetry approach eliminates bulky batteries or capacitors from the ring part. Each ring consists of a sensor coil (the ring part itself), 1-DoF piezoelectric accelerometer, and varactor diode; moreover, it has different resonant frequencies. Typing shocks slightly shift the resonant frequency, and these are detected by a wrist-mounted readout coil. 5-bit chord keyboard is realized by attaching five sensor rings on five fingers. Our evaluation shows that the prototype achieved the tiny (6 g, 3.5 cm^3) ring sensor and 89.7% of typing detection ratio.

Sharing systems for personal mobility devices (PMDs) with batteries are gaining popularity because of their advantages in terms of maneuverability and low environmental burdens. However, PMDs suffer from a small battery capacity, a lack of charging stations, and the inconvenience of plugging in for charging. To solve these problems, we propose retrofitting city spaces into wireless power transfer (WPT) stations/pads by placing sheet-like transmitters in available open spaces. Many studies have considered WPT for electrical vehicles; however, little attention has been given to WPT for PMDs, although they have many unique characteristics to consider (e.g., light weight, form factor, sharing ecosystems, and random running routes). In this study, we investigated a form of WPT via magnetic resonant coupling for PMDs through coil design, analysis, and measurements based on two scenarios: (1) charging PMDs at charging stations and (2) charging in-motion PMDs from chargers placed on the road. Based on these considerations, we confirmed that around 90 % transfer efficiency can be obtained in laboratory setups, whereas practical setups achieved around 70% efficiency.

In this paper, we propose a passive and contactless epidermal pressure sensor patch printed on a paper substrate with silver nano-particle ink. This disposable patch can be used to measure the pressure between the clothes and the human body. Different from the conventional pressure sensors, the pressure can be measured wirelessly without disturbing the motion of the users. The sensor circuit pattern is printed by a conductive inkjet printer and the sensor's pressure value is detected by a reader coil through the change of the capacitance of an LC resonant circuit. We propose a sensor design method that minimizes the effect of the human body. We demonstrate our sensor patch by measuring the pressure exerted by compression garments whose pressure distribution is important for the wearer's health.