Bistatic Passive Sensing via CSI Power

TL;DR

This paper introduces a lightweight, real-time bistatic passive sensing framework that leverages CSI power to improve target tracking and micro-motion sensing without phase calibration, suitable for practical mobile network environments.

Contribution

It proposes a novel CSI-power domain approach that suppresses phase offsets and resolves mirror ambiguity, enabling accurate 3D target tracking in bistatic passive sensing.

Findings

Effective in real-world LTE signals and datasets

Improves tracking accuracy without phase calibration

Demonstrates robustness in diverse sensing scenarios

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.