Super resolution passive radars based on 802.11ax Wi‐Fi signals for human movement detection

Yildirim, Hasan Can; Determe, Jean-François; Storrer, Laurent; Rottenberg, François; Doncker, Philippe De; Louveaux, Jérôme; Horlin, François

doi:10.1049/rsn2.12038

Cited by 9 publications

(10 citation statements)

References 22 publications

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“…Finally, another emerging technique is the use of modern radars to count people or estimate their flow [14]. These radars are non-cooperative systems and can even reuse existing overthe-air transmissions for radar processing (they are then called passive radars).…”

Section: And Table 1]mentioning

confidence: 99%

Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

Determe

Azzagnuni

Singh

et al. 2022

IEEE Systems Journal

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This paper presents a crowd monitoring system based on the passive detection of probe requests. The system meets strict privacy requirements and is suited to monitoring events or buildings with a least a few hundreds of attendees. We present our counting process and an associated mathematical model. From this model, we derive a concentration inequality that highlights the accuracy of our crowd count estimator. Then, we describe our system. We present and discuss our sensor hardware, our computing system architecture, and an efficient implementation of our counting algorithm-as well as its space and time complexity. We also show how our system ensures the privacy of people in the monitored area. Finally, we validate our system using nine weeks of data from a public library endowed with a camera-based counting system, which generates counts against which we compare those of our counting system. This comparison empirically quantifies the accuracy of our counting system, thereby showing it to be suitable for monitoring public areas. Similarly, the concentration inequality provides a theoretical validation of the system.

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Section: And Table 1]mentioning

confidence: 99%

Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

Determe

Azzagnuni

Singh

et al. 2022

IEEE Systems Journal

Self Cite

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“…First, the noise at the receiver which distorts the characteristics of the signal subspace makes it difficult for ASREN to resolve them separately. This particular problem is addressed in [22] by studying the Cramer-Rao Lower Bound of closely spaced paths. Second, we assume that each path is specular in our simulations, that is, there is only a single Dirac Delta function associated with any given target.…”

Section: Estimation Accuracymentioning

confidence: 99%

“…We have proposed a preliminary radar architecture for indoor human/object detection and tracking [22]. However, the radar architecture was limited by: i) only the range and speed estimations; ii) the estimation of signal and noise subspaces through the sample covariance matrix, which incurs a potential loss of information due to the phase terms that cancel/affect each other, and iii) serialized implementation of the proposed architecture.…”

Section: Introductionmentioning

confidence: 99%

A multi‐antenna super‐resolution passive Wi‐Fi radar algorithm: Combined model order selection and parameter estimation

Yildirim

Storrer

Doncker

et al. 2022

IET Radar Sonar & Navi

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In recent years, Wi‐Fi has become the main gateway that connects users to the Internet. Considering the availability of Wi‐Fi signals, and their suitability for channel estimation, IEEE established the Wi‐Fi Sensing (WS) Task Group whose purpose is to study the feasibility of Wi‐Fi‐based environment sensing. However, Wi‐Fi signals are transmitted over limited bandwidths with a relatively small number of antennas in bursts, fundamentally limiting the range, Angle‐of‐Arrival and speed resolutions. This paper presents a super‐resolution algorithm to perform the parameter estimation in a quasi‐monostatic WS scenario. The proposed algorithm, RIVES, estimates the range, Angle‐of‐Arrival and speed parameters with Vandermonde decomposition of Hankel matrices. To estimate the size of the signal subspace, RIVES uses a novel Model Order Selection method which eliminates spurious noise targets based on their distance to the noise and signal subspaces. Various scenarios with multiple targets are simulated to show the robustness of RIVES. In order to prove its accuracy, real‐life indoor experiments are conducted with human targets by using Software Defined Radios.

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“…Device-free NVB solutions, both based on RF or other physical properties (such as audio or ultrasound signals), are less harmful to an individual's privacy since they do not require processing of devices' IDs. This pushes towards emerging NVB techniques using reflected-power approaches [154], also referred to as passive NVB solutions, which reuse existing over-the-air signals to count people or estimate their flow, by viewing the signals as power carriers rather than information ones.…”

Section: Main Challengesmentioning

confidence: 99%

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Darsena¹,

Gelli²,

Iudice³

et al. 2022

Preprint

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<p>Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs); (iii) inform in real-time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus stops/stations, and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.</p>

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Super resolution passive radars based on 802.11ax Wi‐Fi signals for human movement detection

Cited by 9 publications

References 22 publications

Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

Monitoring Large Crowds With WiFi: A Privacy-Preserving Approach

A multi‐antenna super‐resolution passive Wi‐Fi radar algorithm: Combined model order selection and parameter estimation

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

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