A WiFi Multi-Link Collaborative Human Tracking Method for Smart Home

PAN Houcheng; CAI Yushuang; YAO Junmei; ZHANG Tingting

doi:10.11999/JEIT260267

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PAN Houcheng, CAI Yushuang, YAO Junmei, ZHANG Tingting. A WiFi Multi-Link Collaborative Human Tracking Method for Smart Home[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260267

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PAN Houcheng, CAI Yushuang, YAO Junmei, ZHANG Tingting. A WiFi Multi-Link Collaborative Human Tracking Method for Smart Home[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260267

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A WiFi Multi-Link Collaborative Human Tracking Method for Smart Home

doi: 10.11999/JEIT260267 cstr: 32379.14.JEIT260267

PAN Houcheng^{1, 2},
CAI Yushuang^{1, 2},
YAO Junmei³,
ZHANG Tingting^{1, 2
,
,}

1.
School of Information Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
2.
Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing, Shenzhen 518055, China
3.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Funds: The Natural Science Foundation of China (62171160, 62027802), The Fundamental Research Funds for the Central Universities (HIT.OCEF.2022055), The Shenzhen Science and Technology Program (KJZD20231023093055002, KJZD20230923114804009), Guangdong Provincial Key Laboratory (2024) (2024KSYS023)

Received Date: 2026-03-11
Accepted Date: 2026-07-01
Rev Recd Date: 2026-07-01

Available Online: 2026-07-12

Abstract

Abstract

Objective WiFi-based indoor passive human tracking has become increasingly attractive for smart homes due to the widespread interconnection of Internet of Things (IoT) devices via existing WiFi infrastructures. Some approaches estimate Angle of Arrival (AoA) or Time of Flight (ToF) of the target reflection path to achieve tracking, yet which are fundamentally limited by the difficulty of distinguishing weak target reflection component from dense multipath due to small antenna array and narrow bandwidth under the existing mature protocols. Consequently, Doppler Frequency Shift (DFS)-based approaches utilizing multiple links have proven more practical. Dead Reckoning (DR) is commonly applied in such methods. However, it is challenging to dynamically select links for signal fusion. Furthermore, the performance of DR-based methods is significantly degraded when device locations are inaccurate. Additionally, Existing methods usually adopt a single-transmitter-multi-receiver structure, which complicates data aggregation and restricts multi-antenna utilization. To address the challenges of multi-link signal fusion and device-location uncertainty, Particle Filter (PF) is used in this paper. Furthermore, a multi-transmitter-single-receiver structure is adopted, in which multiple STAtions (STAs) transmit uplink packets to a single Access Point (AP). This structure simplifies data aggregation while exploiting the multi-antenna advantage at the AP. Methods In the IEEE 802.11 standard, the wireless channel is estimated at the receiver using pilots embedded in WiFi packets, and Channel State Information (CSI) is continuously reported. Because human motion perturbs multipath propagation and causes time-varying changes in CSI, CSI is utilized as the primary sensing signal since it implicitly encodes the information of target’s movement. In practice, raw CSI is strongly corrupted by amplitude and phase impairments. Therefore, noise reduction is first conducted before Doppler extraction. Subsequently, multi-link DFS are fused via PF, in which the target state is sequentially propagated and updated using likelihoods derived from Doppler measurements. This formulation naturally facilitates dynamic link selection and probabilistic fusion, as unreliable links are down-weighted through their likelihood contributions rather than being deterministically discarded. Moreover, the inaccuracy of device locations can be absorbed into measurement noise, contributing to the robustness against device-location uncertainty. In the absence of prior knowledge of device locations, self-localization is enabled first by jointly estimating the AoA and ToF of the Line of Sight (LoS) path between the AP and each STA. Preliminary self-localization tests of devices (Fig. 5) demonstrate a median STA positioning error of approximately 0.56 m (Table 1), which provides a relatively reliable initialization for the tracking algorithm. Results and Discussions A prototype is implemented for evaluation under a multi-transmitter-single-receiver architecture mounted with commodity Intel AX200/AX201 WiFi cards. CSI is collected in a 20 MHz bandwidth channel at a 5.24 GHz center frequency using PicoScenes, with 802.11ac packets transmitted at 100 to 200 Hz. Each CSI sample contains measurements of 57 subcarriers. In parallel, FTM measurement is performed at 2.4 GHz in channel 11 using iw and hostapd. In the prototype, each STA transmitter is equipped with a single antenna, while the AP receiver is equipped with two antennas. Experiments are conducted with one AP and three to four STAs (Fig. 7). WiTraj and PITrack using DR are set as baselines. When device self-localization is required, the proposed method achieves a median tracking error of 0.47 m, compared to 1.78 m for WiTraj and 1.77 m for PITrack (Fig. 9). This demonstrates an accuracy improvement of approximately 70% over traditional DR-based methods. Error injection experiments involving random positional disturbances reveal that, unlike traditional DR-based methods which are highly sensitive to location mismatch, the proposed approach exhibits robustness to device-location uncertainty (Fig. 10). Additionally, computational complexity analyses indicate that while execution time increases with the number of particles (Table 3), tracking performance stabilizes beyond a certain threshold (Fig. 11). Thus, real-time operation is attainable without sacrificing tracking quality by selecting an optimal particle count. Finally, evaluations in complex environments confirm that the proposed method consistently maintains accurate tracking performance, which is better than the baselines (Fig. 12). Conclusions A Particle-Filter-based approach is proposed for multi-link collaborative passive human tracking using commodity WiFi. By probabilistically fusing multiple links, robust tracking is achieved even when device locations contain errors. When device locations are unavailable, self-localization is supported through combining CSI-based estimation and FTM measurements, which supplies the necessary geometric priors for tracking. A prototype is built on a multi-transmitter-single-receiver network structure, which facilitates data aggregation and exploits AP-side multiple-antenna advantage. Experimental results indicate that (1) a sub-meter level tracking accuracy can be achieved when there exist device-location errors, which is much higher than the traditional method; (2) the proposed method has certain robustness to device-location uncertainty; and (3) the computational cost can meet real-time requirements when the particle count is properly chosen. However, the current study focuses on single-person tracking only. Future work should extend the framework to multi-person scenarios and evaluate performance under more diverse and realistic environmental conditions.
- WiFi sensing,
- Multi-link,
- Passive human tracking,
- Doppler Frequency Shift (DFS)

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References(21)

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