Proceedings ArticleDOI
Opportunistic Doppler-Only Indoor Localization Via Passive Radar
Wenda Li,Bo Tan,Robert J. Piechocki +2 more
- pp 467-473
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TLDR
Experimental results indicate that the proposed concepts can be used for indoor localization with a high degree of accuracy.Abstract:
Indoor localization is a vital ingredient for many e-Healthcare and Ambient Assisted Living (AAL) applications. However, accurate, low power and user acceptable solutions remain elusive. In this paper, we present a novel opportunistic system which estimates the localization information based only on the Doppler information from the user. The Doppler information is collected using the passive radar technique that deploys the RF energy transfer signal which originally intended only to deliver energy to home IoT devices. A low complexity Extended Kalman Filter (EKF) is also proposed to predict and track the user's location. A real-time system has been built based on the software defined radio (SDR) platform to verify the proposed methodology. Experimental results indicate that the proposed concepts can be used for indoor localization with a high degree of accuracy.read more
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Journal ArticleDOI
On CSI and Passive Wi-Fi Radar for Opportunistic Physical Activity Recognition
TL;DR: In this paper , the authors compare the performance of passive Wi-Fi radar (PWR) and channel state information (CSI) based wireless sensing (SENS) for human activity detection.
Journal ArticleDOI
Temporal Self-Supervised Learning for RSSI-based Indoor Localization
Jonas Paulavicius,Seifallah Jardak,Ryan McConville,Robert J. Piechocki,Raul Santos-Rodriguez +4 more
TL;DR: The feasibility of integrating the temporal nature of the Bluetooth Low Energy Received Signal Strength Indicator into a self-supervised machine learning model for room-level and sub-room-level localization in a realistic residential setting is investigated and maximum likelihood estimation in a conditional random field model and contrastive learning are explored.
Proceedings ArticleDOI
Temporal Self-Supervised Learning for RSSI-based Indoor Localization
TL;DR: In this paper , the feasibility of integrating the temporal nature of the BLE RSSI into a self-supervised machine learning model for room-level and sub-room-level localization in a realistic residential setting is investigated.
References
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Proceedings ArticleDOI
Accurate indoor localization with zero start-up cost
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Proceedings ArticleDOI
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Rainer Mautz,Sebastian Tilch +1 more
TL;DR: A survey of current optical indoor positioning approaches is provided and different systems are briefly described and categorized based on how the images are referenced to the environment.
Journal ArticleDOI
Localization of Narrowband Radio Emitters Based on Doppler Frequency Shifts
Alon Amar,Anthony J. Weiss +1 more
TL;DR: A single-step approach based on the maximum likelihood criterion is proposed here for both known and unknown waveforms and it is shown that in some cases of interest the proposed method inherently selects reliable observations while ignoring unreliable data.