BAIR: A Fine-Grained Real-Time Multi-Modal Ranging System on Smartphones

Accurate and quick relative-distance measurement is crucial for supporting various intelligent transparent services, such as multi-device collaboration, screen rotation, and multi-device mirroring. Unfortunately, current methods often rely on single-modality sensing, resulting in various limitations: BLE-based and WiFi-based methods suffer from coarse-grained estimation, and ultrasound-based approaches suffer from limited sensing range. In this work, we aim at designing a distance-measurement system that enjoys long range, high accuracy, and small delay. Our designed system, named BAIR, relies on low-energy Bluetooth (BLE), acoustic sensors, and inertial measurement units (IMU) equipped on commercial smartphones for fine-grained and real-time relative distance estimation. BAIR effectively aligns multiple sensory signals with different sampling rates via the improved Kalman filter technology. To mitigate IMU's integration errors, BAIR calculates the average velocity over a preceding period and uses this, alongside accumulated velocity data from the IMU, significantly improving distance prediction accuracy. We implemented our BAIR system on smartphones and conducted extensive experiments to evaluate its performance. Specifically, in static scenarios, BAIR achieves a mean average error (MAE) of 11 cm. In moving scenarios, the cumulative distribution function (CDF) values for 95%, 80%, and 50% are 31 cm, 13 cm, and 8 cm, respectively. The memory footprint of BAIR is 16.41 MB. We release a video demo on YouTube<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://youtu.be/7Fbmn4ALaI0.