# Robust Indoor Positioning with Hybrid WiFi RTT-RSS Signals

## TL;DR

This paper introduces a new WiFi-based indoor positioning system that provides accurate location estimates and uncertainty regions.

## Contribution

The novel use of WiFi RTT-RSS with Conformal Prediction to enable uncertainty-aware indoor positioning.

## Key findings

- The method achieves a positioning accuracy of 0.6 meters.
- It provides prediction regions with theoretical coverage guarantees for circular regions.
- This is one of the first works to enable uncertainty quantification with WiFi ranging signals.

## Abstract

In 2025, there is still no ubiquitous, accurate, infrastructure-free indoor positioning system. Among existing approaches, WiFi-based positioning is highly promising as it leverages existing infrastructure. However, its performance is severely affected by WiFi signal variability and environmental dynamics. Thus, this paper presents a novel approach that combines WiFi Round Trip Time and Received Signal Strength measurements with the Conformal Prediction (CP) framework to achieve robust uncertainty-aware indoor positioning. Our proposed method does not only accurately estimate the user position, but also provides two prediction regions: a rectangular region and a circular region. We systematically evaluate our method across three real-world testbeds, which achieves a positioning accuracy of 0.6 m, while generating prediction regions with theoretical coverage guarantees for circular regions and marginal coverage guarantees for rectangular regions. To the best of our knowledge, this is one of the first work to enable uncertainty quantification on top of state-of-the-art WiFi ranging signals.