A Multi-Static Radar Network with Ultra-Wideband Radio-Equipped Devices

Ledergerber, Anton; D’Andrea, Raffaello

doi:10.3390/s20061599

Cited by 34 publications

(56 citation statements)

References 40 publications

(48 reference statements)

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“…Information (including raw signal, bandpass signal, motion filtered signal, and detection list) extracted from the reflected signals is used to estimate the locations of targets with an accuracy at the decimeter level. To improve the accuracy, a multi-static is deployed in [129] to track a person in real-time by determining the difference between the channel impulse response with the presence of a new object and that of the previous one without the object. The location of the object can be found with the mean error of only 3 cm by applying a leading edge detection algorithm on the difference between This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: A: Crowd Detectionmentioning

confidence: 99%

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

et al. 2020

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Social distancing plays a pivotal role in preventing the spread of viral diseases illnesses such as COVID-19. By minimizing the close physical contact among people, we can reduce the chances of catching the virus and spreading it across the community. This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social distancing practice. In this Part I, we provide a comprehensive background of social distancing including basic concepts, measurements, models, and propose various practical social distancing scenarios. We then discuss enabling wireless technologies which are especially effective and can be widely adopted in practice to keep distance, encourage, and enforce social distancing in general. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. The companion paper Part II [1] surveys other emerging and related technologies, such as machine learning, computer vision, thermal, ultrasound, etc., and discusses open issues and challenges (e.g., privacypreserving, scheduling, and incentive mechanisms) in implementing social distancing in practice.

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Section: A: Crowd Detectionmentioning

confidence: 99%

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

et al. 2020

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“…Global Navigation Satellite System (GNSS), and Zigbee signals are potential solutions for social distancing verification (Al-Suwaidi and Zemerly, 2009, Laoudias et al, 2018, Ledergerber and D'Andrea, 2020, Mazuelas et al, 2009, Niu et al, 2015. Sensor technologies pick up ultrasound, inertial, visible light or thermal frequency signals from specific tags attached to the users (Jovicic and Engineer, 2016, Kok et al, 2015, Szajewska, 2017, Tiemann and Wietfeld, 2017.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving AI-enabled video surveillance for social distancing: responsible design and deployment for public spaces

Sugianto

Tjondronegoro

Stockdale

et al. 2021

ITP

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PurposeThe paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.Design/methodology/approachThe paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.FindingsThe proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.Originality/valueThe paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

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“…In addition, they demonstrated that they can detect vehicles with channel sounders [ 14 ]. In [ 15 ], Ledergerber et al present a multi-static radar solution based on UWB transceivers. To localize the person, they inspect the mean and the variance of the measured CIR and determine the position of a person by a particle filter.…”

Section: Related Workmentioning

confidence: 99%

MAMPI-UWB—Multipath-Assisted Device-Free Localization with Magnitude and Phase Information with UWB Transceivers

Cimdins

Schmidt

Hellbrück

2020

Sensors

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In this paper, we propose a multipath-assisted device-free localization (DFL) system that includes magnitude and phase information (MAMPI). The DFL system employs ultra-wideband (UWB) channel impulse response (CIR) measurements, enabling the extraction of several multipath components (MPCs) and thereby benefits from multipath propagation. We propose a radio propagation model that calculates the effect on the received signal based on the position of a person within a target area. Additionally, we propose a validated error model for the measurements and explain the creation of different feature vectors and extraction of the MPCs from Decawave DW1000 CIR measurements. We evaluate the system via simulations of the position error probability and a measurement setup in an indoor scenario. We compare the performance of MAMPI to a conventional DFL system based on four sensor nodes that measures radio signal strength values. The combination of the magnitude and phase differences for the feature vectors results in a position error probability that is comparable to a conventional system but requires only two sensor nodes.

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A Multi-Static Radar Network with Ultra-Wideband Radio-Equipped Devices

Cited by 34 publications

References 40 publications

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part I: Fundamentals and Enabling Technologies

Privacy-preserving AI-enabled video surveillance for social distancing: responsible design and deployment for public spaces

MAMPI-UWB—Multipath-Assisted Device-Free Localization with Magnitude and Phase Information with UWB Transceivers

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