Deep Learning-Driven Adaptive-Weight Kalman Filtering for Low-Cost GNSS in Challenging Environments | JoVE Visualize

Area of Science:

Geomatics Engineering
Signal Processing
Machine Learning

Background:

Smartphone Global Navigation Satellite System (GNSS) observations suffer from multipath and non-line-of-sight (NLOS) errors in urban environments.
Traditional stochastic models (e.g., elevation-dependent, SNR-based) are inadequate for capturing complex, variable observation errors in dense urban canyons.
Accurate parameter estimation requires robust stochastic models to handle GNSS measurement uncertainties.

Purpose of the Study:

To develop an adaptive GNSS stochastic model using a deep neural network (DNN) to improve positioning accuracy in challenging urban environments.
To integrate signal-to-noise ratio (SNR), satellite elevation angle, and post-fit pseudorange residuals as input features for the DNN.
To adaptively weight GNSS measurements epoch-wise within a Kalman filtering process.

Main Methods:

A fully connected DNN was designed to learn the nonlinear relationship between input features (SNR, elevation angle, residuals) and measurement uncertainty.
The DNN output was used to adaptively update the measurement noise covariance matrix for real-time weighting.
The proposed model was evaluated using data from a u-blox ZED-F9P receiver and a Samsung Galaxy S21+ smartphone during vehicle experiments in urban canyons.

Main Results:

The DNN-based stochastic model significantly outperformed conventional models in single point positioning (SPP) accuracy for both smartphone and receiver data.
Smartphone 3D RMSE decreased from ~13-14m to 8.94m (approx. 35% improvement) in obstructed urban environments.
Receiver 3D RMSE improved from ~4-5m to 3.10m, demonstrating consistent performance gains.

Conclusions:

The proposed DNN-based stochastic model effectively mitigates complex GNSS observation errors in urban settings.
This approach offers a promising solution for reliable and accurate positioning using low-cost GNSS receivers, especially on smartphones.
Adaptive weighting based on DNNs enhances robustness and accuracy in challenging geomatics applications.