# Localization with Noisy Android Raw GNSS Measurements

arXiv: https://arxiv.org/abs/2309.08936 · HTML: https://mlsanity.com/paper/2309.08936/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2023.

## Built on by (most cited first, 2 of 2)

- [Towards End-to-End GPS Localization with Neural Pseudorange Correction](https://mlsanity.com/paper/2401.10685/) (2024)
- [NeRC: Neural Ranging Correction through Differentiable Moving Horizon Location Estimation](https://mlsanity.com/paper/2508.14336/) (2025)

## Abstract (arXiv)

Android raw Global Navigation Satellite System (GNSS) measurements are expected to bring smartphones power to take on demanding localization tasks that are traditionally performed by specialized GNSS receivers. The hardware constraints, however, make Android raw GNSS measurements much noisier than geodetic-quality ones. This study elucidates the principles of localization using Android raw GNSS measurements and leverages Moving Horizon Estimation (MHE), Extended Kalman Filter (EKF), and Rauch-Tung-Striebel (RTS) smoother for noise suppression. Experimental results show that the RTS smoother achieves the best positioning performance, with horizontal positioning errors significantly reduced by 76.4% and 46.5% in static and dynamic scenarios compared with the baseline weighted least squares (WLS) method. Our codes are available at https://github.com/ailocar/androidGnss.