Search

Passive Wi-Fi-based radars (PWRs) are devices thatenable the localization of targets using Wi-Fi signals of opportunity transmitted by an access point. Unlike active radars thatoptimize their transmitted waveform for localization, PWRs alignwith the 802.11 amendments. Specifically, during the channelsounding session preceding a multi-user multiple-input multipleoutput downlink transmission, an access point isotropically transmits a null data packet (NDP) with a known preamble. Fromthese known symbols, client user equipments derive their channelstate information and transmit an unencrypted beamformingfeedback (BFF) back to the access point. The BFF comprisesthe right singular matrix of the channel and the correspondingstream gain for each subcarrier, which allows the computationof a beamforming matrix at the access point. In a classicalPWR processing, only the preamble symbols from the NDP areexploited during the channel sounding session. In this study,we investigate multiple target localization by a PWR exploitinghybrid information sources. On one hand, the joint angle-of departure and angle-of-arrival evaluated from the NDP. Onanother hand, the line-of-sight angle-of-departures inferred fromthe BFFs. The processing steps at the PWR are defined and anoptimal hybrid fusion rule is derived in the maximum likelihoodframework. Monte-Carlo simulations assess the enhanced accuracy of the proposed combination method compared to classicalPWR processing based solely on the NDP, and compare thelocalisation performance between client and non-client targets., info:eu-repo/semantics/inPress

In this study, we experimentally validated the possibility of estimating the angle of departure (AoD) using multiple signal classification (MUSIC) with only WiFi control frames for beamforming feedback (BFF), defined in IEEE 802.11ac/ax. The examined BFF-based MUSIC is a model-driven algorithm that does not require a pre-obtained database. This is in contrast with most existing BFF-based sensing techniques, which are data-driven and require a pre-obtained database. Moreover, BFF-based MUSIC affords an alternative AoD estimation method without requiring access to the channel state information (CSI). Extensive experimental and numerical evaluations demonstrate that BFF-based MUSIC can successfully estimate the AoDs for multiple propagation paths. Moreover, the evaluations performed in this study reveal that BFF-based MUSIC, where BFF is a highly compressed version of CSI in IEEE 802.11ac/ax, achieves an error of AoD estimation that is comparable to that of CSI-based MUSIC.

In the field of WiFi sensing, as an alternative sensing source of the channel state information (CSI) matrix, the use of a beamforming feedback matrix (BFM) that is a right singular matrix of the CSI matrix has attracted significant interest owing to its wide availability regarding the underlying WiFi systems. In the IEEE 802.11ac/ax standard, the station (STA) transmits a BFM to an access point (AP), which uses the BFM for precoded multiple-input and multiple-output communications. In addition, in the same way, the AP transmits a BFM to the STA, and the STA uses the received BFM. Regarding BFM-based sensing, extensive real-world experiments were conducted as part of this study, and two key insights were reported: Firstly, this report identified a potential issue related to accuracy in existing uni-directional BFM-based sensing frameworks that leverage only BFMs transmitted for the AP or STA. Such uni-directionality introduces accuracy concerns when there is a sensing capability gap between the uni-directional BFMs for the AP and STA. Thus, this report experimentally evaluates the sensing ability disparity between the uni-directional BFMs, and shows that the BFMs transmitted for an AP achieve higher sensing accuracy compared to the BFMs transmitted from the STA when the sensing target values are estimated depending on the angle of departure of the AP. Secondly, to complement the sensing gap, this paper proposes a bi-directional sensing framework, which simultaneously leverages the BFMs transmitted from the AP and STA. The experimental evaluations reveal that bi-directional sensing achieves higher accuracy than uni-directional sensing in terms of the human localization task.

In the field of WiFi sensing, as an alternative sensing source of the channel state information (CSI) matrix, the use of a beamforming feedback matrix (BFM) that is a right singular matrix of the CSI matrix has attracted significant interest owing to its wide availability regarding the underlying WiFi systems. In the IEEE 802.11ac/ax standard, the station (STA) transmits a BFM to an access point (AP), which uses the BFM for precoded multiple-input and multiple-output communications. In addition, in the same way, the AP transmits a BFM to the STA, and the STA uses the received BFM. Regarding BFM-based sensing, extensive real-world experiments were conducted as part of this study, and two key insights were reported: Firstly, this report identified a potential issue related to accuracy in existing uni-directional BFM-based sensing frameworks that leverage only BFMs transmitted for the AP or STA. Such uni-directionality introduces accuracy concerns when there is a sensing capability gap between the uni-directional BFMs for the AP and STA. Thus, this report experimentally evaluates the sensing ability disparity between the uni-directional BFMs, and shows that the BFMs transmitted for an AP achieve higher sensing accuracy compared to the BFMs transmitted from the STA when the sensing target values are estimated depending on the angle of departure of the AP. Secondly, to complement the sensing gap, this paper proposes a bi-directional sensing framework, which simultaneously leverages the BFMs transmitted from the AP and STA. The experimental evaluations reveal that bi-directional sensing achieves higher accuracy than uni-directional sensing in terms of the human localization task.

In this study, we experimentally validated the possibility of estimating the angle of departure (AoD) using multiple signal classification (MUSIC) with only WiFi control frames for beamforming feedback (BFF), defined in IEEE 802.11ac/ax. The examined BFF-based MUSIC is a model-driven algorithm that does not require a pre-obtained database. This is in contrast with most existing BFF-based sensing techniques, which are data-driven and require a pre-obtained database. Moreover, BFF-based MUSIC affords an alternative AoD estimation method without requiring access to the channel state information (CSI). Extensive experimental and numerical evaluations demonstrate that BFF-based MUSIC can successfully estimate the AoDs for multiple propagation paths. Moreover, the evaluations performed in this study reveal that BFF-based MUSIC, where BFF is a highly compressed version of CSI in IEEE 802.11ac/ax, achieves an error of AoD estimation that is comparable to that of CSI-based MUSIC.