Experimental Implementation of a Multi-Antenna 802.11ax-based Passive Bistatic Radar for Human Target Detection

Storrer, Laurent; Yildirim, Hasan Can; Copa, Evert Pocoma; Louveaux, Jérôme; Doncker, Philippe De; Pollin, Sofie; Horlin, François

doi:10.1109/radarconf2043947.2020.9266626

Cited by 4 publications

(9 citation statements)

References 10 publications

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“…By going back in time-domain and computing the sample offset between the exponentials at each antenna, the phase difference Φ 0 − Φ l can be estimated and cancelled by multiplying the received signal by the scalar exp(j(Φ 0 − Φ l )). Details about the phase calibration can be found in [8].…”

Section: B Hardware-induced Phase Calibrationmentioning

confidence: 99%

“…However, in this work, a local maxima search on each RDM is preferred to avoid detecting the range and Doppler significant points caused by the leakage (cf. Sections II and III-D) [8]. In the general case, all targets, denoted with an index r, might not be detected, and ghost targets created by higher-order MPCs can also be detected.…”

Section: E Target Detectionmentioning

confidence: 99%

“…The calibration is critical since without it the angle estimation is erroneous due to hardware-induced phase shift Φ l in (1). Errors of a few samples in the sample offset estimation during the calibration result in an angle estimation error of the order of one degree [8]. More details on this discussion and the estimation process are given in [8].…”

Section: F Angle Processingmentioning

confidence: 99%

“…It can be acquired by pointing a highly directive antenna towards TX, following a so-called reference channel approach [2]. A method requiring fewer resources is to use the known training fields at the beginning of each Wi-Fi packet [5] [7] [8]. A multi-antenna PBR also allows one to exploit the phase difference in the received signal between antennas to compute the angle-of-arrival (AoA) of the MPCs corresponding to targets and typically outputs a so-called range-Doppler-angle cube (RDAC) [8].…”

Section: Introductionmentioning

confidence: 99%

“…Preamble field provides sufficient accuracy for tracking, therefore getting rid of the need for a reference channel; • We use only one 4-antenna Uniform Linear Array receiver built with Universal Software Radio Peripherals (USRPs) calibrated over-the-air to cancel hardwareinduced parasitic phase shifts, based on our previous work on the matter [8]. We propose a novel method for reference antenna selection when antenna face detection or synchronization differences.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Indoor Tracking of Multiple Individuals With an 802.11ax Wi-Fi-Based Multi-Antenna Passive Radar

Storrer

Yildirim

Crauwels³

et al. 2021

IEEE Sensors J.

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We investigate indoor human multi-target tracking in cartesian coordinates based on range, Doppler and Angle-of-Arrival measurements obtained with a four-antenna passive bistatic radar capturing 802.11ax Wi-Fi signals. A reference antenna selection method is described to perform angle processing correctly when dealing with target detection diversity among antennas. The tracking is performed by an Unscented Kalman Filter (UKF) to handle the non-linear relation between the measurement space and the state space. A Joint Probabilistic Data Association Filter is coupled to the UKF to handle the data association between tracks and measurements when dealing with multiple targets. Simulations are performed to determine the tracking parameters under heavy constraints and identify key scenarios. An experimental setup is built using Universal Software Radio Peripherals, featuring an over-the-air phase calibration for angle processing with an anchor antenna. It is used to validate the proposed single and multi-target tracking scheme.

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Section: B Hardware-induced Phase Calibrationmentioning

confidence: 99%

Section: E Target Detectionmentioning

confidence: 99%

Section: F Angle Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Indoor Tracking of Multiple Individuals With an 802.11ax Wi-Fi-Based Multi-Antenna Passive Radar

Storrer

Yildirim

Crauwels³

et al. 2021

IEEE Sensors J.

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

Yildirim

Determe

Storrer

et al. 2021

IET Radar Sonar & Navi

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Passive Radars based on Wi-Fi signals provide an excellent opportunity for human sensing without violating the privacy of individuals. Due to the limited integration time of Wi-Fi bursts and relatively low bandwidths, Fourier Transform-based methods do not provide the required accuracy. Herein, a Wi-Fi-based passive radar algorithm is proposed for indoor human movement detection with super resolution which relies on the ESPRIT algorithm to estimate range/speed parameters from limited number of measurements. To determine the number of targets in the environment, a new Model Order Selection (MOS) method is proposed which exploits the orthogonality between the basis vectors of signal and noise subspaces obtained from the sample covariance matrix of the measurements. The new MOS method along with the proposed algorithm are numerically analysed and compared with other existing methods. Finally, the performance of the algorithm is experimentally validated in indoor conditions. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Multiradar Data Fusion for Respiratory Measurement of Multiple People

2021

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This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system.

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Experimental Implementation of a Multi-Antenna 802.11ax-based Passive Bistatic Radar for Human Target Detection

Cited by 4 publications

References 10 publications

Indoor Tracking of Multiple Individuals With an 802.11ax Wi-Fi-Based Multi-Antenna Passive Radar

Indoor Tracking of Multiple Individuals With an 802.11ax Wi-Fi-Based Multi-Antenna Passive Radar

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

Multiradar Data Fusion for Respiratory Measurement of Multiple People

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