Slocalization: Sub-{\mu}W Ultra Wideband Backscatter Localization

Ultra wideband technology has shown great promise for providing high-quality location estimation, even in complex indoor multipath environments, but existing ultra wideband systems require tens to hundreds of milliwatts during operation. Backscatter communication has demonstrated the viability of astonishingly low-power tags, but has thus far been restricted to narrowband systems with low localization resolution. The challenge to combining these complimentary technologies is that they share a compounding limitation, constrained transmit power. Regulations limit ultra wideband transmissions to just -41.3 dBm/MHz, and a backscatter device can only reflect the power it receives. The solution is long-term integration of this limited power, lifting the initially imperceptible signal out of the noise. This integration only works while the target is stationary. However, stationary describes the vast majority of objects, especially lost ones. With this insight, we design Slocalization, a sub-microwatt, decimeter-accurate localization system that opens a new tradeoff space in localization systems and realizes an energy, size, and cost point that invites the localization of every thing. To evaluate this concept, we implement an energy-harvesting Slocalization tag and find that Slocalization can recover ultra wideband backscatter in under fifteen minutes across thirty meters of space and localize tags with a mean 3D Euclidean error of only 30 cm.Comment: Published at the 17th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN'18

Phase-based variant maximum likelihood positioning for passive UHF-RFID tags

Radio frequency identification (MD) technology brings tremendous advancement in Internet-of-Things, especially in supply chain and smart inventory management. Phase-based passive ultra high frequency RFID tag localization has attracted great interest, due to its insensitivity to the propagation environment and tagged object properties compared with the signal strength based method. In this paper, a phase-based maximum-likelihood tag positioning estimation is proposed. To mitigate the phase uncertainty, the likelihood function is reconstructed through trigonometric transformation. Weights are constructed to reduce the impact of unexpected interference and to augment the positioning performance. The experiment results show that the proposed algorithms realize line-grained tag localization, which achieve centimeter-level lateral accuracy, and less than 15-centimeters vertical accuracy along the altitude of the racks

INDOOR-WIRELESS LOCATION TECHNIQUES AND ALGORITHMS UTILIZING UHF RFID AND BLE TECHNOLOGIES

The work presented herein explores the ability of Ultra High Frequency Radio Frequency (UHF RF) devices, specifically (Radio Frequency Identification) RFID passive tags and Bluetooth Low Energy (BLE) to be used as tools to locate items of interest inside a building. Localization Systems based on these technologies are commercially available, but have failed to be widely adopted due to significant drawbacks in the accuracy and reliability of state of the art systems. It is the goal of this work to address that issue by identifying and potentially improving upon localization algorithms. The work presented here breaks the process of localization into distance estimations and trilateration algorithms to use those estimations to determine a 2D location. Distance estimations are the largest error source in trilateration. Several methods are proposed to improve speed and accuracy of measurements using additional information from frequency variations and phase angle information. Adding information from the characteristic signature of multipath signals allowed for a significant reduction in distance estimation error for both BLE and RFID which was quantified using neural network optimization techniques. The resulting error reduction algorithm was generalizable to completely new environments with very different multipath behavior and was a significant contribution of this work. Another significant contribution of this work is the experimental comparison of trilateration algorithms, which tested new and existing methods of trilateration for accuracy in a controlled environment using the same data sets. Several new or improved methods of triangulation are presented as well as traditional methods from the literature in the analysis. The Antenna Pattern Method represents a new way of compensating for the antenna radiation pattern and its potential impact on signal strength, which is also an important contribution of this effort. The performance of each algorithm for multiple types of inputs are compared and the resulting error matrix allows a potential system designer to select the best option given the particular system constraints

A Long-range Fine-scale RF Positioning System Using Tunneling Tags

Fine-scale positioning systems using inexpensive, low-power, and reliable smart tags enables numerous commercial and scientific applications. Internet of Things (IoT) applications, such as asset tracking, contact tracing, and autonomous driving, require wireless technologies with both the long ranges of conventional wireless links and the low power consumption of passive and semi-passive Radio Frequency Identification (RFID) tags. This dissertation proves that using the Received Signal Phase (RSP)-based positioning method and Tunneling tags at 5.8 GHz breaks the range limit of fine-scale RFID positioning systems. A frequency hopping reader operating in the 5.8 GHz Industrial, Scientific and Medical (ISM) band is designed and implemented in this work. Experimental results yield a one-dimensional distance estimation error of less than 1% at ranges of 100 m when a clear Line-of-Sight (LoS) is available in indoor and outdoor environments. Compared to Received Signal Strength (RSS)-based positioning techniques, the average positioning accuracy is improved by a factor of 51 at ranges of tens of meters. In Non-Line-of-Sight (NLoS) scenarios, the proposed system achieves an estimation error of less than 1.9%. Experimental results also demonstrate that the RSP-based positioning technique allows estimating a mobile reader's two-dimensional position with an average error of 0.17 m in an outdoor environment. Also, a channel sounder implementation using the same hardware configuration further increases the accuracy in multipath environments. Calculation based on the system specifications projects a sub-meter level accuracy at ranges of more than 1 km is feasible using the proposed method.Ph.D

ReLoc: Hybrid RSSI- and phase-based relative UHF-RFID tag localization with COTS devices

Radio frequency identification (RFID) technology brings tremendous advancements in the Industrial Internet of Things (IIoT), especially for smart inventory management, as it provides a fast and low-cost way of counting or positioning items in the warehouse. In the last decade, many novel solutions, including absolute and relative positioning methods, have been proposed for this application. However, the available methods are quite sensitive to the minor changes in the deployment scenario, including the orientation of the tag and antenna, the materials contained inside the carton, tag distortion, and multipath propagation. To this end, we propose a hybrid relative passive RFID localization method (ReLoc) based on both the received signal strength indicator (RSSI) and measured phases, which orders the RFID tags horizontally and vertically. In this article, the phase-based variant maximum likelihood estimation is proposed for lateral positioning, and the RSSI profiles of two tilted antennas are compared with each other for level distinguishing. We implement the proposed positioning system ReLoc with commercial off-the-shelf RFID devices. The experiment in a warehouse shows that ReLoc is a powerful solution for practical item-level inventory management. The experimental results show that ReLoc achieves an average lateral and level ordering accuracy of 94.6% and 94.3%, respectively. Notably, when considering liquid or metal materials inside the carton or tag distortion, ReLoc still performs excellently with more than 93% ordering accuracy both horizontally and vertically, indicating the robustness of the proposed method

Wireless Positioning and Tracking for Internet of Things in GPS-denied Environments

Wireless positioning and tracking have long been a critical technology for various applications such as indoor/outdoor navigation, surveillance, tracking of assets and employees, and guided tours, among others. Proliferation of Internet of Things (IoT) devices, the evolution of smart cities, and vulnerabilities of traditional localization technologies to cyber-attacks such as jamming and spoofing of GPS necessitate development of novel radio frequency (RF) localization and tracking technologies that are accurate, energy-efficient, robust, scalable, non-invasive and secure. The main challenges that are considered in this research work are obtaining fundamental limits of localization accuracy using received signal strength (RSS) information with directional antennas, and use of burst and intermittent measurements for localization. In this dissertation, we consider various RSS-based techniques that rely on existing wireless infrastructures to obtain location information of corresponding IoT devices. In the first approach, we present a detailed study on localization accuracy of UHF RF IDentification (RFID) systems considering realistic radiation pattern of directional antennas. Radiation patterns of antennas and antenna arrays may significantly affect RSS in wireless networks. The sensitivity of tag antennas and receiver antennas play a crucial role. In this research, we obtain the fundamental limits of localization accuracy considering radiation patterns and sensitivity of the antennas by deriving Cramer-Rao Lower Bounds (CRLBs) using estimation theory techniques. In the second approach, we consider a millimeter Wave (mmWave) system with linear antenna array using beamforming radiation patterns to localize user equipment in an indoor environment. In the third approach, we introduce a tracking and occupancy monitoring system that uses ambient, bursty, and intermittent WiFi probe requests radiated from mobile devices. Burst and intermittent signals are prominent characteristics of IoT devices; using these features, we propose a tracking technique that uses interacting multiple models (IMM) with Kalman filtering. Finally, we tackle the problem of indoor UAV navigation to a wireless source using its Rayleigh fading RSS measurements. We propose a UAV navigation technique based on Q-learning that is a model-free reinforcement learning technique to tackle the variation in the RSS caused by Rayleigh fading

Improving RF Localization Through Measurement and Manipulation of the Channel Impulse Response

For over twenty years, global navigation satellite systems like GPS have provided an invaluable navigation, tracking, and time synchronization service that is used by people, wildlife, and machinery. Unfortunately, the coverage and accuracy of GPS is diminished or lost when brought indoors since GPS signals experience attenuation and distortion after passing through and reflecting off of building materials. This disparity in coverage coupled with growing demands for indoor positioning, navigation, and tracking has led to a plethora of research in localization technologies. To date, however, no single system has emerged as a clear solution to the indoor localization and navigation problem because the myriad of potential applications have widely varying performance requirements and design constraints that no system satisfies. Fortunately, recently-introduced commercial ultra-wideband RF hardware offers excellent ranging accuracy in difficult indoor settings, but these systems lack the robustness and simplicity needed for many indoor applications. We claim that an asymmetric design that separates transmit and receive functions can enable many of the envisioned applications not currently realizable with an integrated design. This separation of functionality allows for a flexible architecture which is more robust to the in-band interference and heavy multipath commonly found in indoor environments. In this dissertation, we explore the size, weight, accuracy, and power requirements imposed on tracked objects (tags) for three broadly representative applications and propose the design of fixed-location infrastructure (anchors) that accurately and robustly estimate a tag’s location, while minimizing deployment complexity and adhering to a unified system architecture. Enabled applications range from 3D tracking of small, fast-moving micro-quadrotors to 2D personal navigation across indoor maps to tracking objects that remain stationary for long periods of time with near-zero energy cost. Each application requires careful measurement of the ultra-wideband channel impulse response, and an augmented narrowband receiver is proposed to perform these measurements. The key design principle is to offload implementation complexity to static infrastructure where an increase in cost and complexity can be more easily absorbed and amortized. Finally, with an eye towards the future, we explore how the increasingly crowded RF spectrum impacts current ultra-wideband system design, and propose an alternative architecture that enables improved coexistence of narrowband and ultra-wideband transmissions.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138642/1/bpkempke_1.pd