# Device-Free Human State Estimation using UWB Multi-Static Radios

arXiv: https://arxiv.org/abs/2401.05410 · HTML: https://mlsanity.com/paper/2401.05410/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 26 Dec 2023.

## Builds on (most cited first, 9 of 9)

- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [PYSKL: Towards Good Practices for Skeleton Action Recognition](https://mlsanity.com/paper/2205.09443/) (2022)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [An Overview of Ultra-WideBand (UWB) Standards and Organizations (IEEE 802.15.4, FiRa, Apple): Interoperability Aspects and Future Research Directions](https://mlsanity.com/paper/2202.02190/) (2022)
- [A Dataset of Human Motion Status Using IR-UWB Through-wall Radar](https://mlsanity.com/paper/2008.13598/) (2020)
- [PEOPLEx: PEdestrian Opportunistic Positioning LEveraging IMU, UWB, BLE and WiFi](https://mlsanity.com/paper/2311.18182/) (2023)
- [Multi-Static UWB Radar-based Passive Human Tracking Using COTS Devices](https://mlsanity.com/paper/2109.12856/) (2021)
- [Message Flow Analysis with Complex Causal Links for Distributed ROS 2 Systems](https://mlsanity.com/paper/2204.10208/) (2022)
- [Analyze, Debug, Optimize: Real-Time Tracing for Perception and Mapping Systems in ROS 2](https://mlsanity.com/paper/2204.11778/) (2022)

## Built on by (most cited first, 1 of 1)

- [UWB-PostureGuard: A Privacy-Preserving RF Sensing System for Continuous Ergonomic Sitting Posture Monitoring](https://mlsanity.com/paper/2508.11115/) (2025)

## Abstract (arXiv)

We present a human state estimation framework that allows us to estimate the location, and even the activities, of people in an indoor environment without the requirement that they carry a specific devices with them. To achieve this "device free" localization we use a small number of low-cost Ultra-Wide Band (UWB) sensors distributed across the environment of interest. To achieve high quality estimation from the UWB signals merely reflected of people in the environment, we exploit a deep network that can learn to make inferences. The hardware setup consists of commercial off-the-shelf (COTS) single antenna UWB modules for sensing, paired with Raspberry PI units for computational processing and data transfer. We make use of the channel impulse response (CIR) measurements from the UWB sensors to estimate the human state - comprised of location and activity - in a given area. Additionally, we can also estimate the number of humans that occupy this region of interest. In our approach, first, we pre-process the CIR data which involves meticulous aggregation of measurements and extraction of key statistics. Afterwards, we leverage a convolutional deep neural network to map the CIRs into precise location estimates with sub-30 cm accuracy. Similarly, we achieve accurate human activity recognition and occupancy counting results. We show that we can quickly fine-tune our model for new out-of-distribution users, a process that requires only a few minutes of data and a few epochs of training. Our results show that UWB is a promising solution for adaptable smart-home localization and activity recognition problems.