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WiSLAT: A Simultaneous Device Localization and Target Tracking Method for Wi-Fi Systems

Chunxi Chen, Jingwen Zhang, Chao Yu, Fan Liu, Rui Wang

Abstract

It has been shown that the channel state information (CSI) of a Wi-Fi system can be exploited to localize Wi-Fi devices or track trajectory of a moving target. In the existing literature, both sensing tasks are treated separately and some prior information is usually requested, including the signal fingerprints, the locations of some anchor devices in the Wi-Fi system, and etc. In the proposed WiSLAT method, however, it is shown that both sensing tasks can assist each other, such that the request on prior system information can be eliminated. Particularly, in a Wi-Fi system with an access point (AP) and at least three stations, where the locations of the stations are unknown, the WiSLAT is designed to detect the Doppler frequencies of the downlink CSI at the stations, such that their locations and the trajectory of the target with respect to the AP can be inferred. The joint detection can be conducted by searching the optimal stations' locations and target's trajectory, such that their corresponding Doppler frequencies fit the observed ones best. Due to the tremendous non-convex search space, a low-complexity sub-optimal algorithm integrating alternate optimization, extended Kalman filter and density-based clustering is proposed in WiSLAT. Experiments conducted in indoor environments demonstrate the effectiveness of WiSLAT, achieving a median trajectory-tracking error of 0.68 m.

WiSLAT: A Simultaneous Device Localization and Target Tracking Method for Wi-Fi Systems

Abstract

It has been shown that the channel state information (CSI) of a Wi-Fi system can be exploited to localize Wi-Fi devices or track trajectory of a moving target. In the existing literature, both sensing tasks are treated separately and some prior information is usually requested, including the signal fingerprints, the locations of some anchor devices in the Wi-Fi system, and etc. In the proposed WiSLAT method, however, it is shown that both sensing tasks can assist each other, such that the request on prior system information can be eliminated. Particularly, in a Wi-Fi system with an access point (AP) and at least three stations, where the locations of the stations are unknown, the WiSLAT is designed to detect the Doppler frequencies of the downlink CSI at the stations, such that their locations and the trajectory of the target with respect to the AP can be inferred. The joint detection can be conducted by searching the optimal stations' locations and target's trajectory, such that their corresponding Doppler frequencies fit the observed ones best. Due to the tremendous non-convex search space, a low-complexity sub-optimal algorithm integrating alternate optimization, extended Kalman filter and density-based clustering is proposed in WiSLAT. Experiments conducted in indoor environments demonstrate the effectiveness of WiSLAT, achieving a median trajectory-tracking error of 0.68 m.
Paper Structure (14 sections, 2 theorems, 47 equations, 7 figures)

This paper contains 14 sections, 2 theorems, 47 equations, 7 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Motion Model
  4. Doppler Detection via CSI
  5. WiSLAT Problem Formulation
  6. Low-Complexity Solution Algorithm
  7. Optimization of $\mathbf{R}$
  8. Optimization of $\boldsymbol{p}_1$
  9. EXPERIMENTS AND DISCUSSIONS
  10. Experimental Settings
  11. Experimental Results
  12. Conclusion
  13. EKF for Trajectory Reconstruction
  14. Gradient Descent for $\boldsymbol{p}_{\mathrm{RX},m}$

Key Result

Lemma 1

As illustrated in fig:motionmodel, given the position $\boldsymbol{p}_n$ and velocity $\boldsymbol{v}_n$ of the moving target at the $n$-th time instance, as well as the actual Doppler frequency $f^d_m(n)$ at the Rx-$m$, the angle rotatingThe anticlockwise rotation leads to a positive angle, and clo Moreover, denote $\boldsymbol{u}(\boldsymbol{v}_n,\beta_{m,n})$ as the unit direction vector of $\b

Figures (7)

  • Figure 1: An scenario illustration of simultaneous devices localization and trajectory tracking (SLAT) with one AP and four receive stations.
  • Figure 2: An example of time-frequency spectrogram in the Doppler detection.
  • Figure 3: An workflow of low-complexity sub-optimal algorithm integrating alternate optimization.
  • Figure 4: Kinematic diagram of a Tx-Rx pair and a moving target.
  • Figure 5: Experiment setup in an indoor corridor scenario.
  • ...and 2 more figures

Theorems & Definitions (3)

  • Lemma 1: Potential Directions of Receive Station
  • Lemma 2: Gradient of $g_m$
  • Remark 1