面向智能家居的WiFi多链路协同人体跟踪方法

潘厚丞; 蔡宇双; 姚俊梅; 张霆廷

doi:10.11999/JEIT260267

面向智能家居的WiFi多链路协同人体跟踪方法

doi: 10.11999/JEIT260267 cstr: 32379.14.JEIT260267

潘厚丞^{1, 2},
蔡宇双^{1, 2},
姚俊梅³,
张霆廷^{1, 2, ,}

1.
哈尔滨工业大学(深圳)信息科学与技术学院深圳 518055
2.
广东省空天网络与智能感知重点实验室深圳 518055
3.
深圳大学计算机与软件学院深圳 518060

基金项目: 国家自然科学基金(62171160, 62027802)，深圳市重大科技专项(KJZD20231023093055002, KJZD20230923114804009)，广东省重点实验项目(2024) (2024KSYS023)，广东省自然科学基金面上项目 (2025A1515010125)

详细信息

作者简介:
潘厚丞：男，硕士生，研究方向为信号处理、通信感知一体化等

蔡宇双：男，博士生，研究方向为Wi-Fi感知、多传感器融合、多视角学习等

姚俊梅：女，副教授，研究方向为无线网络优化、通信感知一体化、移动计算等

张霆廷：男，教授，研究方向为多智能体协同理论与实现、无线定位理论和脉冲超宽带技术等

通讯作者:
张霆廷　zhangtt@hit.edu.cn

中图分类号: TN929.5
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出版历程
- 收稿日期: 2026-03-11
- 修回日期: 2026-06-30
- 录用日期: 2026-07-01
- 网络出版日期: 2026-07-12

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

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 National Natural Science Foundation of China (62171160, 62027802), Shenzhen Science and Technology Program (KJZD20231023093055002, KJZD20230923114804009), Key Laboratory project in Guangdong Province (2024) (2024KSYS023), General Project of Guangdong Provincial Natural Science Foundation (2025A1515010125)

摘要

摘要: 在智能家居场景中，WiFi网络将多个物联网(IoT)设备相连，为实现室内被动人体跟踪提供了新路径。然而，多链路跟踪需要考虑信号融合、设备自定位等问题。为此，该文提出一种适用于商用WiFi的多链路协同跟踪方法。针对商用网卡的测量信号噪声，该方法首先进行信号预处理，从高维的原始信道状态信息(CSI)数据中提取多普勒频移(DFS)。随后，对多链路DFS进行融合，以实现协同跟踪。该方法在没有WiFi设备位置先验知识的条件下也能实现设备自定位，为跟踪算法提供输入。最后，该文搭建了一个多发单收网络结构的原型验证系统，在未知设备位置的情况下实现了小于50 cm的中位跟踪误差，相比传统方法提升了约70%。在复杂场景的测试中表明，该系统能达到亚米级的跟踪精度，对设备位置误差具有鲁棒性，并显示出一定实时运行潜力。
- WiFi感知 /
- 多链路 /
- 被动人体跟踪 /
- 多普勒频移
Abstract: Objective WiFi-based indoor passive human tracking has attracted increasing attention for smart home applications because Internet of Things (IoT) devices are widely interconnected through existing WiFi infrastructures. Existing approaches estimate the Angle of Arrival (AoA) or Time of Flight (ToF) of the target-reflected path to achieve tracking. However, these methods are fundamentally limited by the difficulty of separating weak target-reflected signals from dense multipath propagation when commercial WiFi devices provide only a small antenna array and limited bandwidth under existing communication protocols. Therefore, Doppler Frequency Shift (DFS)-based approaches that exploit multiple links have become more practical. Dead Reckoning (DR) is widely adopted in these methods, but dynamic link selection for signal fusion remains challenging. Furthermore, the performance of DR-based methods degrades substantially when device-location errors are present. Existing methods also generally employ a single-transmitter–multi-receiver architecture, in which Access Point (AP) broadcasts downlink WiFi signals to STAtions (STA). It complicates data aggregation and limits the use of multiple receive antennas of AP's. To address the challenges of multi-link signal fusion and device-location uncertainty, a Particle Filter (PF)-based method is proposed. Furthermore, a multi-transmitter–single-receiver architecture is adopted, in which multiple STAs transmit uplink packets to a single AP. This architecture simplifies data aggregation while exploiting the AP’s multi-antenna capability. Methods In the IEEE 802.11 standard, the wireless channel is estimated at the receiver using pilot signals embedded in WiFi packets, and Channel State Information (CSI) is continuously obtained. Human motion perturbs multipath propagation and produces time-varying changes in CSI. Therefore, CSI serves as the primary sensing signal because it implicitly captures target motion. In practice, raw CSI is affected by amplitude and phase impairments. Accordingly, signal preprocessing is first performed before Doppler extraction. Subsequently, multi-link DFS measurements are fused using PF, in which the target state is sequentially propagated and updated according to likelihoods derived from DFS measurements. This probabilistic framework naturally enables dynamic multi-link fusion because unreliable links receive lower weights rather than being deterministically discarded. Moreover, the effects of device-location errors are incorporated into the measurement-noise model, improving robustness to device-location uncertainty. When prior knowledge of device locations is unavailable, device self-localization is first performed by jointly estimating the Line-of-Sight (LoS) AoA and ToF between the AP and each STA. Preliminary device self-localization experiments (Fig. 5) demonstrate a median STA localization error of approximately 0.56 m (Table 1), providing reliable initialization for the subsequent tracking algorithm. Results and Discussions A prototype system is implemented using a multi-transmitter–single-receiver architecture with commercial Intel AX200/AX201 WiFi cards. CSI is collected over a 20 MHz channel centered at 5.24 GHz using PicoScenes, with IEEE 802.11ac packets transmitted at 100 or 200 Hz. Each CSI sample contains measurements from 57 subcarriers. Meanwhile, Fine Time Measurement (FTM) is performed on Channel 11 at 2.4 GHz using iw and hostapd. In the prototype system, each STA transmitter is equipped with a single antenna, while the AP receiver employs two antennas. Experiments are conducted using one AP and three or four STAs (Fig. 7). WiTraj and PITrack, both based on DR, are used as baseline methods. When device self-localization is required, the proposed method achieves a median tracking error of 0.47 m, compared with 1.78 m for WiTraj and 1.77 m for PITrack (Fig. 9), representing an accuracy improvement of approximately 70% over conventional DR-based methods. Error-injection experiments with random device-location perturbations show that, unlike conventional DR-based methods, which are highly sensitive to device-location errors, the proposed method remains robust to device-location uncertainty (Fig. 10). Additionally, computational complexity analysis shows that, although execution time increases with the particle count (Table 3), tracking performance reaches a stable level beyond a moderate particle count (Fig. 11). Therefore, real-time operation can be achieved by selecting an appropriate particle count. Finally, experiments in complex environments demonstrate that the proposed method consistently achieves higher tracking accuracy than the baseline methods (Fig. 12). Conclusions A PF-based method is proposed for multi-link collaborative passive human tracking using commercial WiFi devices. By probabilistically fusing measurements from multiple links, robust tracking is achieved even when device-location errors are present. When device locations are unavailable, device self-localization is achieved by combining CSI-based estimation with FTM measurements, providing the geometric information required for tracking. A prototype system based on a multi-transmitter–single-receiver architecture is developed to simplify data aggregation while exploiting the AP’s multi-antenna capability. Experimental results demonstrate that the proposed method achieves sub-0.5 m median tracking error when device-location errors are present, representing an improvement of approximately 70% over conventional DR-based methods. The proposed method also exhibits strong robustness to device-location uncertainty and supports real-time implementation when an appropriate particle count is selected. Future work will extend the framework to multi-person tracking and evaluate its performance under more realistic deployment conditions.
- WiFi sensing /
- Multi-link /
- Passive human tracking /
- Doppler Frequency Shift (DFS)

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图 1 智能家居中的WiFi多链路跟踪系统模型

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图 2 系统流程图

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图 3 设备自定位示意图

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图 4 FTM协议原理示意图

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图 5 设备自定位测试场景示意图

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图 6 多普勒提取过程示意图

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图 7 实验场景示意图

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图 8 部分跟踪结果示意图

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图 9 跟踪误差的累积分布函数曲线

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图 10 设备位置误差对跟踪性能的影响

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图 11 不同参数下的跟踪性能

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图 12 复杂环境测试场景与结果示意图

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表 1 设备自定位测试结果

位置编号	1	2	3	4	5	6	7	8	9	10	11	12	13	14
AoA估计误差 (°)	6.2	8.2	5.0	11.6	13.4	0.0	3.0	10.4	2.0	16.0	22.6	5.4	1.8	5.4
FTM测距误差 (m)	0.07	0.00	0.35	0.17	0.11	0.21	0.08	0.33	0.54	0.42	0.29	0.82	0.68	0.59
STA定位误差 (m)	0.33	0.36	0.42	0.65	0.56	0.21	0.14	0.56	0.54	0.63	1.00	0.84	0.68	0.62

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1 基于粒子滤波的多链路跟踪

输入：每条链路$ n $的DFS序列$ \left\{\overrightarrow{f_{\text{D}}^{n}}\right\} $，每个STA $ n $的位置$ \left(x_{\text{STA}}^{n},y_{\text{STA}}^{n}\right) $，链路数$ N $，粒子数$ {N}_{\text{S}} $，初始状态分布$ \boldsymbol{P} $，过程噪声协方差矩阵　$ \boldsymbol{Q} $，测量噪声方差$ {\sigma }^{2} $，时间间隔$ \Delta T $
输出：目标的估计状态$ {\left\{{\hat{\boldsymbol{x}}}[t]\right\}}_{t=1,2,\cdots ,T} $
1 for $ i $ = 1 to $ {N}_{\text{S}} $ do
2 　从初始状态分布中采样第$ i $个粒子：$ {\boldsymbol{x}}_{i}[1]\sim \boldsymbol{P} $；设置第$ i $个粒子的初始权重：$ {w}_{i}[1]=1/{N}_{\text{S}} $
3 end for
4 获取粒子初始状态：$ {\hat{\boldsymbol{x}}}[1]=\displaystyle\sum \nolimits_{i=1}^{{N}_{\text{S}}}{w}_{i}[1]{\boldsymbol{x}}_{i}[1] $
5 for $ t $ = 2 to $ T $ do
6 　for $ i $ = 1 to $ {N}_{\text{S}} $ do
7 　　通过式(15)传播粒子$ {\boldsymbol{x}}_{i}[t] $
8 　　for $ n $ = 1 to $ N $ do
9 　　　通过式(16)估计第$ i $个粒子的DFS $ \hat{f}_{\text{D}i}^{n}[t] $；通过式(17)计算第$ i $个粒子的似然度$ {p}_{i} $；计算权重$ {w}_{i}[t]={w}_{i}[t]\times {p}_{i} $
10 　 end for
11 end for
12 权重归一化：$ {w}_{i}[t]={w}_{i}[t]/\displaystyle\sum \nolimits_{i=1}^{{N}_{\text{S}}}{w}_{i}[t] $；估计目标状态：$ {\hat{\boldsymbol{x}}}[t]=\displaystyle\sum \nolimits_{i=1}^{{N}_{\text{S}}}{w}_{i}[t]{\boldsymbol{x}}_{i}[t] $
13 重采样$ {N}_{\text{S}} $个粒子
14 for $ i $ = 1 to $ {N}_{\text{S}} $ do
15 　　重置权重：$ {w}_{i}[t]=1/{N}_{\text{S}} $
16 end for
17 end for
18 return $ {\left\{{\hat{\boldsymbol{x}}}[t]\right\}}_{t=1,2,\cdots ,T} $

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表 2 基于粒子滤波的多链路跟踪算法参数设置

$ {N}_{\text{S}} $	$ \boldsymbol{P} $	$ \boldsymbol{Q} $	$ {\sigma }^{2} $	$ \Delta T $
1000	$ \mathcal{N}\left(\left[\begin{array}{c}{x}_{\text{tar}}[1]\\{y}_{\text{tar}}[1]\\0\\0\end{array}\right],\text{diag}\left[\begin{array}{c}{0}^{2}\\{0}^{2}\\{0.01}^{2}\\{0.01}^{2}\end{array}\right]\right) $	$ \mathcal{N}\left(\left[\begin{array}{c}0\\0\\0\\0\end{array}\right],\text{diag}\left[\begin{array}{c}{0.05}^{2}\\{0.05}^{2}\\{0.25}^{2}\\{0.25}^{2}\end{array}\right]\right) $	0.4²	0.1 s

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表 3 不同参数下的算法复杂度

参数	N	3				4
参数	N_S	500	1000	2000	5000	500	1000	2000	5000
运行时间 (s)	平均值	0.0138	0.0275	0.0551	0.1465	0.0137	0.0270	0.0553	0.1521
运行时间 (s)	最大值	0.0454	0.0808	0.1061	0.2952	0.0440	0.0654	0.1110	0.5602

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