RNN-Based GNSS Positioning using Satellite Measurement Features and Pseudorange Residuals
This work addresses positioning accuracy challenges in GNSS for applications like navigation, but it is incremental as it builds on existing machine learning methods for measurement optimization.
The paper tackles the problem of selecting accurate pseudorange measurements in GNSS positioning by using a recurrent neural network to predict measurement quality factors, achieving results that outperform traditional weighting and selection strategies.
In the Global Navigation Satellite System (GNSS) context, the growing number of available satellites has lead to many challenges when it comes to choosing the most accurate pseudorange contributions, given the strong impact of biased measurements on positioning accuracy, particularly in single-epoch scenarios. This work leverages the potential of machine learning in predicting link-wise measurement quality factors and, hence, optimize measurement weighting. For this purpose, we use a customized matrix composed of heterogeneous features such as conditional pseudorange residuals and per-link satellite metrics (e.g., carrier-to-noise power density ratio and its empirical statistics, satellite elevation, carrier phase lock time). This matrix is then fed as an input to a recurrent neural network (RNN) (i.e., a long-short term memory (LSTM) network). Our experimental results on real data, obtained from extensive field measurements, demonstrate the high potential of our proposed solution being able to outperform traditional measurements weighting and selection strategies from state-of-the-art.