GNSS Positioning Techniques in Urban Environments

Global Navigation Satellite System (GNSS) positioning in urban areas faces significant degradation due to signal blockage, reflection and diffraction by buildings. Multipath propagation and non-line-of-sight (NLOS) receptions introduce biases in pseudorange and carrier-phase measurements, undermining accuracy. Traditional remedies include Differential GNSS (DGNSS) and real-time kinematic (RTK) corrections, which rely on reference stations to reduce common errors. Geometry-based methods such as shadow matching and ray-tracing with three-dimensional city models predict satellite visibility and exclude faulty measurements. Receiver Autonomous Integrity Monitoring (RAIM) further enhances reliability by detecting and omitting gross measurement errors. More recently, machine learning and deep learning techniques have emerged to classify and weight individual satellite signals, exploiting features like carrier-to-noise ratio, pseudorange variance and correlator outputs. Hybrid approaches integrate mode-switching algorithms that alternate between DGNSS and multipath mitigation, achieving seamless service in deep urban canyons. Together, these advances underpin applications in autonomous vehicles, urban logistics and emergency response, where robust metre- or centimetre-level positioning is critical.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Research from all publishers

Recent studies have advanced data-driven classification and mitigation of urban GNSS errors. A generative convolutional neural network converts multivariate time-series GNSS observables into image representations, enabling high-fidelity line-of-sight versus NLOS classification and adaptive signal weighting to enhance positioning accuracy. Another investigation employs a random forest trained on signal quality indicators—carrier-to-noise ratio, pseudorange standard deviation and satellite elevation—to detect and exclude NLOS measurements, reporting improvements of up to 69% horizontally and 79% vertically. A complementary approach introduces a real-time mode-switching algorithm between DGNSS and a bespoke multipath mitigation regime, compensating inter-constellation biases and boosting availability from 64% to 100% while reducing root-mean-square error to approximately 1.2 metres in dense urban canyons—all without reliance on external sensors.

GNSS Positioning Techniques in Urban Environments publication trend

The graph below shows the total number of articles in gnss positioning techniques in urban environments across all publications each year (not limited to Nature Index journals).

Technical terms

Line-of-Sight (LOS): Direct GNSS signal path between satellite and receiver, free of obstructions.

Non-Line-of-Sight (NLOS): Indirect signal arrival after reflection or diffraction, leading to measurement delays.

Multipath: Phenomenon where signals reflect off surfaces, generating multiple delayed replicas.

Differential GNSS (DGNSS): Technique using ground-based reference stations to correct GNSS errors and improve accuracy.

Carrier-to-Noise Ratio (C/N0): Ratio of signal power to noise density, indicating satellite signal quality.

Convolutional Neural Network (CNN): Deep learning model optimised for extracting spatial features from grid-structured data such as images.

References

1. CarNet: A generative convolutional neural network-based line-of-sight/non-line-of-sight classifier for global navigation satellite systems by transforming multivariate time-series data into images. Engineering Applications of Artificial Intelligence (2025).
2. Machine learning based GNSS signal classification and weighting scheme design in the built environment: a comparative experiment. Satellite Navigation (2023).
3. Seamless Accurate Positioning in Deep Urban Area Based on Mode Switching Between DGNSS and Multipath Mitigation Positioning. IEEE Transactions on Intelligent Transportation Systems (2023).
4. NLOS Correction/Exclusion for GNSS Measurement Using RAIM and City Building Models. Sensors (2015).