Position estimation via ultra-wide-band signals

The high time resolution of ultra-wide-band (UWB) signals facilitates very precise position estimation in many scenarios, which makes a variety applications possible. This paper reviews the problem of position estimation in UWB systems, beginning with an overview of the basic structure of UWB signals and their positioning applications. This overview is followed by a discussion of various position estimation techniques, with an emphasis on time-based approaches, which are particularly suitable for UWB positioning systems. Practical issues arising in UWB signal design and hardware implementation are also discussed. © 2009 IEEE

Low-complexity UWB-based collision avoidance system for automated guided vehicles

This paper describes a low-complexity collision avoidance system for automated guided vehicles (AGVs) based on active ultra-wide band (UWB) modules. In particular, we consider an industrial warehouse where all the AGVs and target nodes (TNs) (e.g., people) are equipped with active UWB modules. A communication session between a pair of UWB modules permits the exchange of information and the estimation of the distance between them. The UWB module positioned on an AGV is connected to an on-board computer; whenever the UWB module on an AGV receives a message from a TN, it communicates all the received data to the on-board computer that can decide to stop the AGV if the range estimate is below a given threshold. This prevents undesired collisions between the AGV and the TN. In this paper, we present the experimental results of the proposed collision avoidance system obtained using the UWB modules, PulsON 410 ranging and communication modules (P410 RCMs), produced by Time Domain

Double sliding window variance detection-based time-of-arrival estimation in ultra-wideband ranging systems

Ultra-wideband (UWB) ranging via time-of-arrival (TOA) estimation method has gained a lot of research interests because it can take full advantage of UWB capabilities. Energy detection (ED) based TOA estimation technique is widely used in the area due to its low cost, low complexity and ease of implementation. However, many factors affect the ranging performance of the ED-based methods, especially, non-line-of-sight (NLOS) condition and the integration interval. In this context, a new TOA estimation method is developed in this paper. Firstly, the received signal is denoised using a five-level wavelet decomposition, next, a double sliding window algorithm is applied to detect the change in the variance information of the received signal, the first path (FP) TOA is then calculated according to the first variance sharp increase. The simulation results using the CM1 and CM2 IEEE 802.15.4a channel models, prove that our proposed approach works effectively compared with the conventional ED-based methods

Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features

A recent line of research has been investigating deep learning approaches to wireless positioning (WP). Although these WP algorithms have demonstrated high accuracy and robust performance against diverse channel conditions, they also have a major drawback: they require processing high-dimensional features, which can be prohibitive for mobile applications. In this work, we design a positioning neural network (P-NN) that substantially reduces the complexity of deep learning-based WP through carefully crafted minimum description features. Our feature selection is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We also develop a novel methodology for adaptively selecting the size of feature space, which optimizes over balancing the expected amount of useful information and classification capability, quantified using information-theoretic measures on the signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP).Comment: This paper has been accepted in IEEE International Conference on Communications (ICC) 202

Energy Efficient Event Localization and Classification for Nano IoT

Advancements in nanotechnology promises new capabilities for Internet of Things (IoT) to monitor extremely fine-grained events by deploying sensors as small as a few hundred nanometers. Researchers predict that such tiny sensors can transmit wireless data using graphene-based nano-antenna radiating in the terahertz band (0.1-10 THz). Powering such wireless communications with nanoscale energy supply, however, is a major challenge to overcome. In this paper, we propose an energy efficient event monitoring framework for nano IoT that enables nanosensors to update a remote base station about the location and type of the detected event using only a single short pulse. Nanosensors encode different events using different center frequencies with non overlapping half power bandwidth over the entire terahertz band. Using uniform linear array (ULA) antenna, the base station localizes the events by estimating the direction of arrival of the pulse and classifies them from the center frequency estimated by spectral centroid of the received signal. Simulation results confirm that, from a distance of 1 meter, a 6th derivative Gaussian pulse consuming only 1 atto Joule can achieve localization and classification accuracies of 1.58 degree and 98.8%, respectively.Comment: 6 pages, 18 Figures, accepted for publication in IEEE GLOBECOM Conference 201

Enhanced 3D localisation accuracy of body-mounted miniature antennas using ultra-wideband technology in line-of-sight scenarios

This study presents experimental investigations on high-precision localisation methods of body-worn miniature antennas using ultra-wideband (UWB) technology in line-of-sight conditions. Time of arrival data fusion and peak detection techniques are implemented to estimate the three-dimensional (3D) location of the transmitting tags in terms of x, y, z Cartesian coordinates. Several pseudo-dynamic experiments have been performed by moving the tag antenna in various directions and the precision with which these slight movements could be resolved has been presented. Some more complex localisation experiments have also been undertaken, which involved the tracking of two transmitter tags simultaneously. Excellent 3D localisation accuracy in the range of 1-4 cm has been achieved in various experiment settings. A novel approach for achieving subcentimetre 3D localisation accuracy from UWB technology has been proposed and demonstrated successfully. In this approach, the phase centre information of the antennas in a UWB localisation system is utilised in position estimation to drastically improve the accuracy of the localisation measurements to millimetre levels. By using this technique, the average localisation error has been reduced by 86, 31, and 72% for the x-, y-, and z-axis coordinates, respectively.Published versio

An autonomous ultra-wide band-based attitude and position determination technique for indoor mobile laser scanning

Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large, or it has many turns and narrow corridors. This paper proposes using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey

UWB Localization of people-accuracy aspects

Zaied, Salah: UWB Localization of people-accuracy aspects Zusammenfassung UWB-Sensoren sind durch eine sehr große Bandbreite gekennzeichnet. Diese Bandbreite ermöglicht es, Objekte mit einer sehr guten Genauigkeit zu lokalisieren. Passive Objekte, die kein Sender oder Empfänger tragen, sind mittels zurückgestreuten elektromagnetischen Wellen zu lokalisieren. Es existieren unterschiedliche Lokalisierungsmethoden. Die Masterarbeit analysiert unterschiedliche laufzeitbasierten Lokalisierungsansätze. Die Masterarbeite bietet verschiedene Lösungen von dem Lokalisierungsproblem, der mathematisch mit einem System von quadratischen Gleichungen beschrieben ist. Die Lösungen decken folgende Ansätze ab: Linearisierung mittels einer Taylor Reihe Entwicklung, Kreuzung von Ellipsen and sphärische Interpolation. Die Masterarbeit analysiert Genauigkeit von den Algorithmen in unterschiedlichen Einsatzszenarien. Die Lokalisierungsgenauigkeit war anhand der Hauptkomponentenanalyse ausgewertet.UWB sensors feature very large bandwidth. This bandwidth allows very accurate localization of tag-free targets such as people. In this case, an UWB localization system localizes tag-free by means of backscattered electromagnetic waves. There exist different localization approaches. The thesis concerns with the accuracy aspects of time-of-arrival based localization approaches. The thesis provides different solutions to the localization problem which is mathematically described by a system of two dimensional nonlinear equations of the second order like. These solutions cover: Taylor series linearization, intersection of ellipses and the spherical interpolation. The thesis analyses performance of these localization approaches in different simulation scenarios. The principal component analysis was used to evaluate precision of these localization approaches.Ilmenau, Techn. Univ., Master-Arbeit, 201