# A Robust Adaptive Filtering Framework for Smartphone GNSS/PDR-Integrated Positioning

## TL;DR

This paper introduces a new filtering method to improve smartphone positioning accuracy in challenging urban environments by combining GNSS and PDR data.

## Contribution

A novel two-stage Robust Adaptive Cubature Kalman Filter (RACKF) for integrating GNSS and PDR with adaptive noise estimation and outlier suppression.

## Key findings

- The method achieves a horizontal positioning accuracy of 3.28 m (RMSE).
- It outperforms standalone GNSS by 25.97% and improves 3D PDR by 10.39%.
- The framework is infrastructure-free and suitable for outdoor smartphone navigation.

## Abstract

Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation.