1,136 research outputs found
Emitter Location Finding using Particle Swarm Optimization
Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error
XRLoc: Accurate UWB Localization for XR Systems
Understanding the location of ultra-wideband (UWB) tag-attached objects and
people in the real world is vital to enabling a smooth cyber-physical
transition. However, most UWB localization systems today require multiple
anchors in the environment, which can be very cumbersome to set up. In this
work, we develop XRLoc, providing an accuracy of a few centimeters in many
real-world scenarios. This paper will delineate the key ideas which allow us to
overcome the fundamental restrictions that plague a single anchor point from
localization of a device to within an error of a few centimeters. We deploy a
VR chess game using everyday objects as a demo and find that our system
achieves cm median accuracy and cm percentile
accuracy in dynamic scenarios, performing at least better than
state-of-art localization systems. Additionally, we implement a MAC protocol to
furnish these locations for over tags at update rates of Hz, with a
localization latency of ms
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
Accurate position tracking with a single UWB anchor
Accurate localization and tracking are a fundamental requirement for robotic
applications. Localization systems like GPS, optical tracking, simultaneous
localization and mapping (SLAM) are used for daily life activities, research,
and commercial applications. Ultra-wideband (UWB) technology provides another
venue to accurately locate devices both indoors and outdoors. In this paper, we
study a localization solution with a single UWB anchor, instead of the
traditional multi-anchor setup. Besides the challenge of a single UWB ranging
source, the only other sensor we require is a low-cost 9 DoF inertial
measurement unit (IMU). Under such a configuration, we propose continuous
monitoring of UWB range changes to estimate the robot speed when moving on a
line. Combining speed estimation with orientation estimation from the IMU
sensor, the system becomes temporally observable. We use an Extended Kalman
Filter (EKF) to estimate the pose of a robot. With our solution, we can
effectively correct the accumulated error and maintain accurate tracking of a
moving robot.Comment: Accepted by ICRA202
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