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Register a new userWiBall: A Time-Reversal Focusing Ball Method for Indoor Tracking
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Added by
Feng Zhang
Publication date
2017
fields
Electronic Engineering
and research's language is
English
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With the development of the Internet of Things technology, indoor tracking has become a popular application nowadays, but most existing solutions can only work in line-of-sight scenarios, or require regular re-calibration. In this paper, we propose WiBall, an accurate and calibration-free indoor tracking system that can work well in non-line-of-sight based on radio signals. WiBall leverages a stationary and location-independent property of the time-reversal focusing effect of radio signals for highly accurate moving distance estimation. Together with the direction estimation based on inertial measurement unit and location correction using the constraints from the floorplan, WiBall is shown to be able to track a moving object with decimeter-level accuracy in different environments. Since WiBall can accommodate a large number of users with only a single pair of devices, it is low-cost and easily scalable, and can be a promising candidate for future indoor tracking applications.
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Terahertz spectrum is being researched upon to provide ultra-high throughput radio links for indoor applications, e.g., virtual reality (VR), etc. as well as outdoor applications, e.g., backhaul links, etc. This paper investigates a monopulse-based beam tracking approach for limited mobility users relying on sparse massive multiple input multiple output (MIMO) wireless channels. Owing to the sparsity, beamforming is realized using digitally-controlled radio frequency (RF) / intermediate-frequency (IF) phase shifters with constant amplitude constraint for transmit power compliance. A monopulse-based beam tracking technique, using received signal strength indi-cation (RSSI) is adopted to avoid feedback overheads for obvious reasons of efficacy and resource savings. The Matlab implementation of the beam tracking algorithm is also reported. This Matlab implementation has been kept as general purpose as possible using functions wherein the channel, beamforming codebooks, monopulse comparator, etc. can easily be updated for specific requirements and with minimum code amendments.
The time reversal symmetry of the wave equation allows wave refocusing back at the source. However, this symmetry does not hold in lossy media. We present a new strategy to compensate wave amplitude losses due to attenuation. The strategy leverages the instantaneous time mirror (ITM) which generates reversed waves by a sudden disruption of the medium properties. We create a heterogeneous ITM whose disruption is unequal throughout the space to create waves of different amplitude. The time-reversed waves can then cope with different attenuation paths as typically seen in heterogeneous and lossy environments. We consider an environment with biological tissues and apply the strategy to a two-dimensional digital human phantom from the abdomen. A stronger disruption is introduced where forward waves suffer a history of higher attenuation, with a weaker disruption elsewhere. Computer simulations show heterogeneous ITM is a promising technique to improve time reversal refocusing in heterogeneous, lossy, and dispersive spaces.
Robot table tennis systems require a vision system that can track the ball position with low latency and high sampling rate. Altering the ball to simplify the tracking using for instance infrared coating changes the physics of the ball trajectory. As a result, table tennis systems use custom tracking systems to track the ball based on heuristic algorithms respecting the real time constrains applied to RGB images captured with a set of cameras. However, these heuristic algorithms often report erroneous ball positions, and the table tennis policies typically need to incorporate additional heuristics to detect and possibly correct outliers. In this paper, we propose a vision system for object detection and tracking that focus on reliability while providing real time performance. Our assumption is that by using multiple cameras, we can find and discard the errors obtained in the object detection phase by checking for consistency with the positions reported by other cameras. We provide an open source implementation of the proposed tracking system to simplify future research in robot table tennis or related tracking applications with strong real time requirements. We evaluate the proposed system thoroughly in simulation and in the real system, outperforming previous work. Furthermore, we show that the accuracy and robustness of the proposed system increases as more cameras are added. Finally, we evaluate the table tennis playing performance of an existing method in the real robot using the proposed vision system. We measure a slight increase in performance compared to a previous vision system even after removing all the heuristics previously present to filter out erroneous ball observations.
Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) communication systems. In this paper, a novel three-dimensional (3D) space-time-frequency non-stationary massive multiple-input multiple-output (MIMO) channel model for 6G THz indoor communication systems is proposed. In this geometry-based stochastic model (GBSM), the initialization and evolution of parameters in time, space, and frequency domains are developed to generate the complete channel transfer function (CTF). Based on the proposed model, the correlation functions including time auto-correlation function (ACF), spatial crosscorrelation function (CCF), and frequency correlation function (FCF) are investigated. The results show that the statistical properties of the simulation model match well with those of the theoretical model. The stationary intervals at different frequencies are simulated. The non-stationarity in time, space, and frequency domains is verified by theoretical derivations and simulations.
The proliferation of wireless localization technologies provides a promising future for serving human beings in indoor scenarios. Their applications include real-time tracking, activity recognition, health care, navigation, emergence detection, and target-of-interest monitoring, among others. Additionally, indoor localization technologies address the inefficiency of GPS (Global Positioning System) inside buildings. Since people spend most of their time in indoor environments, indoor tracking service is in great public demand. Based on this observation, this paper aims to provide a better understanding of state-of-the-art technologies and stimulate new research efforts in this field. For these purposes, existing localization technologies that can be used for tracking individuals in indoor environments are reviewed, along with some further discussions.
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