Improving GNSS Positioning Using Neural-Network-Based Corrections

Abstract

Deep neural networks (DNNs) are a promising tool for global navigation satellite system (GNSS) positioning in the presence of multipath and non-line-of-sight errors, owing to their ability to model complex errors using data. However, developing a DNN for GNSS positioning presents various challenges, such as (a) poor numerical conditioning caused by large variations in measurements and position values across the globe, (b) varying number and order within the set of measurements due to changing satellite visibility, and (c) overfitting to available data. In this work, we address the aforementioned challenges and propose an approach for GNSS positioning by applying DNN-based corrections to an initial position guess. Our DNN learns to output the position correction using the set of pseudorange residuals and satellite line-of-sight vectors as inputs. The limited variation in these input and output values improves the numerical conditioning for our DNN. We design our DNN architecture to combine information from the available GNSS measurements, which vary both in number and order, by leveraging recent advancements in set-based deep learning methods. Furthermore, we present a data augmentation strategy to reduce overfitting in the DNN by randomizing the initial position guesses. We, first, perform simulations and show an improvement in the initial positioning error when our DNN-based corrections are applied. After this, we demonstrate that our approach outperforms a weighted least squares (WLS) baseline on real-world data. Our implementation is available at github.com/Stanford-NavLab/deep_gnss.

Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

“…The mapping of the correlator output signal to a two-dimensional input image is proposed for classification. A deep CNN was used to detect the correlator output multipath [30] , and this deep CNN method outperformed the SVM method. DL can be applied not only for signal classification but also for positioning correction.…”

5G Indoor Positioning Error Correction Based on 5G-PECNN

“…In related studies, reference [ 37 ] proposed a method to enhance Global Navigation Satellite System (GNSS) positioning by employing deep learning algorithms, utilizing DNNs to learn position corrections based on GNSS measurements. The architecture, using a set-based deep learning approach, accommodates variations in input quantity and order, and a data augmentation strategy is introduced to reduce overfitting.…”

Multipath Effects Mitigation in Offshore Construction Platform GNSS-RTK Displacement Monitoring Using Parallel Temporal Convolution Network

“…These works provide insights that, if a reflector within a given time window can be uniquely identified, then multipath components present in both pseudorange and carrier phase observations during that period can also be uniquely determined. Recently, SNR and pseudorange observations have already gained widespread use in urban canyon positioning [20] and multipath interference identification [8,26] due to their more pronounced multipath components comparing to the carrier phase(as we would specifically illustrated in Section 2 and Fig. 1).…”