350 research outputs found
Indoor wireless communications and applications
Chapter 3 addresses challenges in radio link and system design in indoor scenarios. Given the fact that most human activities take place in indoor environments, the need for supporting ubiquitous indoor data connectivity and location/tracking service becomes even more important than in the previous decades. Specific technical challenges addressed in this section are(i), modelling complex indoor radio channels for effective antenna deployment, (ii), potential of millimeter-wave (mm-wave) radios for supporting higher data rates, and (iii), feasible indoor localisation and tracking techniques, which are summarised in three dedicated sections of this chapter
Research on Impulse Radio Ultra - wideband Positioning Method Based on Combined BP Neural Network and SVM
Intelligent tour guide is a comprehensive service based on tourist\u27s location, which is closely related to Geographic Information System (GIS), mobile positioning technology and Location-Based Service (LBS). But the intelligent tour guide field urgently needs the integrated positioning and navigation technology inside and outside the room. IR-UWB technology is suitable for positioning, tracking, navigation and communication in complex indoor environment, and is considered as the most potential indoor positioning technology to realize seamless connection between indoor and outdoor with outdoor positioning technologies such as GPS. However, one of the main problems facing IR-UWB positioning is Non-Line-Of-Sight (NLOS) error. Based on the advantages of BP neural network and support vector machine, this paper proposes a multi-model fusion algorithm to mitigate the NLOS propagation error of the time difference of arrival (TDOA) and the angle of arrival (AOA) of IR-UWB signal, and then uses TDOA/AOA hybrid positioning that mitigates the NLOS error. Simulation results show that the combined algorithm has stronger NLOS resistance and higher positioning accuracy than the single machine learning algorithm in mitigation NLOS errors
Probabilistic Time of Arrival Localization
In this letter, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that bias our solutions, and that we can rely on a probabilistic model to learn and compensate for them. The resulting localization error is validated using measurements from a live LTE cellular network to be less than 10 meters, representing an order-of-magnitude improvement
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
The Internet of Things (IoT) has started to empower the future of many
industrial and mass-market applications. Localization techniques are becoming
key to add location context to IoT data without human perception and
intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN)
technologies have advantages such as long-range, low power consumption, low
cost, massive connections, and the capability for communication in both indoor
and outdoor areas. These features make LPWAN signals strong candidates for
mass-market localization applications. However, there are various error sources
that have limited localization performance by using such IoT signals. This
paper reviews the IoT localization system through the following sequence: IoT
localization system review -- localization data sources -- localization
algorithms -- localization error sources and mitigation -- localization
performance evaluation. Compared to the related surveys, this paper has a more
comprehensive and state-of-the-art review on IoT localization methods, an
original review on IoT localization error sources and mitigation, an original
review on IoT localization performance evaluation, and a more comprehensive
review of IoT localization applications, opportunities, and challenges. Thus,
this survey provides comprehensive guidance for peers who are interested in
enabling localization ability in the existing IoT systems, using IoT systems
for localization, or integrating IoT signals with the existing localization
sensors
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