Extracting Range-Doppler Information of Moving Targets from Wi-Fi Channel State Information

• URL: http://arxiv.org/abs/2508.02799v1
• Date: Mon, 04 Aug 2025 18:10:18 GMT
• Title: Extracting Range-Doppler Information of Moving Targets from Wi-Fi Channel State Information
• Authors: Jessica Sanson, Rahul C. Shah, Maximilian Pinaroc, Valerio Frascolla,
• Abstract summary: We propose a new signal processing approach that addresses both challenges via three key innovations.<n>Our method achieves cm-level accuracy in range Doppler estimation of moving targets, validated using a commercial Intel WiFi AX211.
• Score: 1.3581639904351783
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents, for the first time, a method to extract both range and Doppler information from commercial Wi-Fi Channel State Information (CSI) using a monostatic (single transceiver) setup. Utilizing the CSI phase in Wi-Fi sensing from a Network Interface Card (NIC) not designed for full-duplex operation is challenging due to (1) Hardware asynchronization, which introduces significant phase errors, and (2) Proximity of transmit (Tx) and receive (Rx) antennas, which creates strong coupling that overwhelms the motion signal of interest. We propose a new signal processing approach that addresses both challenges via three key innovations: Time offset cancellation, Phase alignment correction, and Tx/Rx coupling mitigation. Our method achieves cm-level accuracy in range and Doppler estimation for moving targets, validated using a commercial Intel Wi-Fi AX211 NIC. Our results show successful detection and tracking of moving objects in realistic environments, establishing the feasibility of high-precision sensing using standard Wi-Fi packet communications and off-the-shelf hardware without requiring any modification or specialized full-duplex capabilities.

Related papers

• WiFi CSI Based Temporal Activity Detection via Dual Pyramid Network [38.854696923757466]
  We propose an efficient Dual Pyramid Network that integrates Temporal Signal Semantics and Local Sensitive Response.<n>The Temporal Signal Semantic splits feature learning into high and low-frequency components, using a novel Signed Mask-Attention mechanism to emphasize important areas and downplay unimportant ones.<n>The Local Sensitive Response captures fluctuations without learning. These feature pyramids are then combined using a new cross-attention fusion mechanism.
  arXiv  Detail & Related papers  (2024-12-19T03:00:45Z)
• OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications [53.398544571833135]
  This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924. irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Our signal achieves faster data transmission within the same spectral bandwidth through 5G standard signal configuration.
  arXiv  Detail & Related papers  (2024-06-07T09:20:30Z)
• Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data [55.70071704247794]
  Integrated sensing and communications (ISAC) is pivotal for 6G communications and is boosted by the rapid development of reconfigurable intelligent surfaces (RISs) This paper proposes the X2Track framework, where we model the tracking function by a hierarchical architecture, jointly utilizing multi-modal CSI indicators across multiple bands, and optimize it in a cross-domain manner. Under X2Track, we design an efficient deep learning algorithm to minimize tracking errors, based on transformer neural networks and adversarial learning techniques.
  arXiv  Detail & Related papers  (2024-05-10T08:04:27Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• WiFi-based Spatiotemporal Human Action Perception [53.41825941088989]
  An end-to-end WiFi signal neural network (SNN) is proposed to enable WiFi-only sensing in both line-of-sight and non-line-of-sight scenarios. Especially, the 3D convolution module is able to explore thetemporal continuity of WiFi signals, and the feature self-attention module can explicitly maintain dominant features.
  arXiv  Detail & Related papers  (2022-06-20T16:03:45Z)
• A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition [53.41825941088989]
  A new WiFi-based and video-based neural network (WiNN) is proposed to improve the robustness of activity recognition. Our results show that WiVi data set satisfies the primary demand and all three branches in the proposed pipeline keep more than $80%$ of activity recognition accuracy.
  arXiv  Detail & Related papers  (2022-05-24T10:49:11Z)
• GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi [52.530330427538885]
  WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring. We propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios.
  arXiv  Detail & Related papers  (2022-05-24T10:20:16Z)
• DeepCSI: Rethinking Wi-Fi Radio Fingerprinting Through MU-MIMO CSI Feedback Deep Learning [15.160442408342407]
  DeepCSI is a novel approach to Wi-Fi radio fingerprinting that authenticates MU-MIMO Wi-Fi devices on the move. We extensively evaluate the performance of DeepCSI through a massive data collection campaign performed in the wild with off-the-shelf equipment. Experimental results indicate that DeepCSI correctly identifies the transmitter with an accuracy of up to 98%.
  arXiv  Detail & Related papers  (2022-04-15T18:59:29Z)
• Multi-Band Wi-Fi Sensing with Matched Feature Granularity [37.40429912751046]
  We propose a multi-band Wi-Fi fusion method for Wi-Fi sensing that hierarchically fuses the features from both the fine-grained CSI at sub-6 GHz and the mid-grained beam SNR at 60 GHz. To address the issue of limited labeled training data, we propose an autoencoder-based multi-band Wi-Fi fusion network that can be pre-trained in an unsupervised fashion.
  arXiv  Detail & Related papers  (2021-12-28T05:50:58Z)
• A Photonic-Circuits-Inspired Compact Network: Toward Real-Time Wireless Signal Classification at the Edge [3.841495731646297]
  Large size of machine learning models can make them difficult to implement on edge devices for latency-sensitive downstream tasks. In wireless communication systems, ML data processing at a sub-millisecond scale will enable real-time network monitoring. We propose a novel compact deep network that consists of a photonic-hardware-inspired recurrent neural network model.
  arXiv  Detail & Related papers  (2021-06-25T19:55:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.