"CIRSense Dataset: Real-World 802.11ax CSI Measurements for Wireless Sensing"

"This dataset accompanies the paper \u201cCIRSense: Rethinking WiFi Sensing with Channel Impulse Response\u201d published in IEEE Transactions on Mobile Computing (DOI: 10.1109\/TMC.2026.3678326). It contains real-world WiFi sensing measurements collected with commodity IEEE 802.11ax devices and is intended to support research on channel impulse response (CIR)-based wireless sensing as well as related CSI\/CFR-based sensing tasks. Useful code to process the data can be found on the GitHub repository releasing the CIRSense implementation at https:\/\/github.com\/Oriseven\/CIRSenseThe dataset is designed for contactless human sensing using WiFi signals, with an emphasis on respiration monitoring, distance estimation, RF distortion compensation, and multi-target sensing. Measurements were collected using the PicoScenes platform with an Intel AX200 NIC and a modified ASUS TUF Gaming AX3000 router, operating at 5.25 GHz with 160 MHz bandwidth under the IEEE 802.11ax protocol. Data were recorded across multiple days and environments to capture realistic channel diversity and deployment variability.The dataset covers a range of sensing scenarios, including:LoS respiration sensing at different target distances,Far-range sensing up to long distances,NLoS respiration sensing in residential apartments,Distance estimation with known target locations,Multi-target sensing with simultaneous subjects.Ground-truth annotations are provided for the primary sensing tasks. Respiration experiments include reference respiration rates measured using a NeuLog respiration belt, while ranging experiments include measured target distance labels. The dataset may include raw CSI measurements, processed CIR data, experiment metadata, and ground-truth annotations, depending on the released package.This dataset can be used to reproduce and benchmark algorithms for:CIR reconstruction from partial CSI observations,hardware distortion compensation,dominant-path and dynamic-path alignment,respiration signal extraction,target ranging,and multi-target WiFi sensing.If you find the project useful and use this dataset, please cite our article:@ARTICLE{11457720, author={Kong, Ruiqi and Chen, He}, journal={IEEE Transactions on Mobile Computing},  title={CIRSense: Rethinking WiFi Sensing With Channel Impulse Response},  year={2026}, volume={}, number={}, pages={1-15}, keywords={Sensors;Wireless fidelity;Channel impulse response;Hardware;Delays;Estimation;Wireless communication;Bandwidth;Wireless sensor networks;OFDM;Channel impulse response;distance estimation;respiration sensing;sensing range;WiFi sensing}, doi={10.1109\/TMC.2026.3678326}} "