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Bistatic Passive Sensing via CSI Power

Zhongqin Wang, J. Andrew Zhang, Kai Wu, Kuangda Chen, Min Xu, Y. Jay Guo

TL;DR

Extensive experiments, including simulations, a real-world prototype using 3.1 GHz LTE signals, and an open-source gait recognition dataset, demonstrate the effectiveness of the proposed CSI-power-based framework for bistatic passive tracking and sensing.

Abstract

Passive object sensing with communication signals is a key enabler of perceptive mobile networks and integrated sensing and communication. In practical bistatic deployments, transmitter-receiver asynchrony and hardware impairments introduce time-varying random phase offsets in Channel State Information (CSI). Together with limited bandwidth and small antenna arrays, these effects degrade sensing accuracy. This work proposes a lightweight bistatic passive tracking and sensing framework that operates in the CSI-power domain. CSI power suppresses these offsets without explicit phase calibration, while preserving target-induced sensing cues. We show that physically admissible constraints in the spatial-frequency domain induced by transmitter-receiver geometry can resolve the mirror ambiguity inherent to real-valued CSI power. Building on these properties, we develop a real-time 3D Fourier-domain processing pipeline that jointly recovers spectral (delay), spatial (angle), and temporal (Doppler) signatures. The resulting features are integrated into an online framework with adaptive motion detection, outlier suppression, and extended Kalman filter tracking with deterministic initialization, followed by position-refined micro-Doppler feature extraction for micro-motion sensing. Extensive experiments, including simulations, a real-world prototype using 3.1 GHz LTE signals, and an open-source gait recognition dataset, demonstrate the effectiveness of the proposed CSI-power-based framework for bistatic passive tracking and sensing.

Bistatic Passive Sensing via CSI Power

TL;DR

Extensive experiments, including simulations, a real-world prototype using 3.1 GHz LTE signals, and an open-source gait recognition dataset, demonstrate the effectiveness of the proposed CSI-power-based framework for bistatic passive tracking and sensing.

Abstract

Passive object sensing with communication signals is a key enabler of perceptive mobile networks and integrated sensing and communication. In practical bistatic deployments, transmitter-receiver asynchrony and hardware impairments introduce time-varying random phase offsets in Channel State Information (CSI). Together with limited bandwidth and small antenna arrays, these effects degrade sensing accuracy. This work proposes a lightweight bistatic passive tracking and sensing framework that operates in the CSI-power domain. CSI power suppresses these offsets without explicit phase calibration, while preserving target-induced sensing cues. We show that physically admissible constraints in the spatial-frequency domain induced by transmitter-receiver geometry can resolve the mirror ambiguity inherent to real-valued CSI power. Building on these properties, we develop a real-time 3D Fourier-domain processing pipeline that jointly recovers spectral (delay), spatial (angle), and temporal (Doppler) signatures. The resulting features are integrated into an online framework with adaptive motion detection, outlier suppression, and extended Kalman filter tracking with deterministic initialization, followed by position-refined micro-Doppler feature extraction for micro-motion sensing. Extensive experiments, including simulations, a real-world prototype using 3.1 GHz LTE signals, and an open-source gait recognition dataset, demonstrate the effectiveness of the proposed CSI-power-based framework for bistatic passive tracking and sensing.

Paper Structure

This paper contains 65 sections, 74 equations, 14 figures, 1 table.

Table of Contents

  1. Introduction
  2. Bistatic CSI Model
  3. Non-Ideal CSI Model
  4. Static and Dynamic Channel Components
  5. Coherent Processing Interval Assumption
  6. Unambiguous Sensing Parameter Estimation via CSI Power
  7. CSI Power for Random Phase Removal
  8. Sensing-Relevant Cross Term
  9. 3D Fourier Feature Extraction
  10. Mirror Component Suppression
  11. From DFT Bins to Physical Parameters
  12. Delay
  13. AoA
  14. Doppler
  15. Practical Grid Refinement
  16. ...and 50 more sections

Figures (14)

  • Figure 1: Geometry of the bistatic system, where the target moves on one side of the Tx-Rx baseline.
  • Figure 2: Micro-Doppler heatmaps: (a) baseline without spatial focusing, and (b) position-refined micro-Doppler via delay-AoA focusing.
  • Figure 3: Experimental setup using a 3.1 GHz LTE signal.
  • Figure 4: WiFi CSI measurement geometry of GaitID dataset zhang2020gaitid.
  • Figure 5: Doppler-only tracking performance under different conditions (multi-receiver, CASR for Doppler estimation).
  • ...and 9 more figures