# RAPID: Retrofitting IEEE 802.11ay Access Points for Indoor Human Detection and Sensing

arXiv: https://arxiv.org/abs/2109.04819 · HTML: https://mlsanity.com/paper/2109.04819/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2021.

## Built on by (most cited first, 4 of 4)

- [A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications](https://mlsanity.com/paper/2112.05593/) (2021)
- [Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction](https://mlsanity.com/paper/2306.14233/) (2023)
- [Synergistic Localization and Sensing in MIMO-OFDM Systems via Mixed-Integer Bilevel Learning](https://mlsanity.com/paper/2507.07118/) (2025)
- [Beyond Sub-6 GHz: Leveraging mmWave Wi-Fi for Gait-Based Person Identification](https://mlsanity.com/paper/2510.08160/) (2025)

## Abstract (arXiv)

In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. Unlike existing approaches for human sensing at millimeter-wave frequencies, which rely on special-purpose radars, RAPID achieves radar-level sensing accuracy with IEEE 802.11ay access points, thus avoiding the burden of installing ad-hoc sensors. RAPID enables contactless human sensing applications, such as people tracking, Human Activity Recognition (HAR), and person identification without requiring modifications to the standard packet structure. Specifically, we leverage IEEE 802.11ay beam training to accurately localize and track multiple individuals within the same environment. Then, we propose a new way of using beam tracking to extract micro-Doppler signatures from the time-varying Channel Impulse Response (CIR) estimated from reflected packets. Such signatures are fed to a deep learning classifier to perform HAR and person identification. RAPID is implemented on a cutting-edge IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, and evaluated on a large dataset of CIR measurements. It is robust across different environments and subjects, and outperforms state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching HAR and person identification accuracies of 94% and 90%, respectively.