Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

Wang, Hao; Pan, Shuguo; Gao, Wang; Xia, Yan

doi:10.3390/mi13071128

Cited by 15 publications

(8 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first level uses SVM and XGBoost, and the second level uses logistic regression (LR) to detect NLOS. A series of works [ 16 , 17 , 18 ] used an unsupervised method of K-means clustering to detect multipath/NLOS. However, ML-based methods have poor generalization performance in different environments, resulting in the detection accuracy of current ML methods still needing to be improved.…”

Section: Related Workmentioning

confidence: 99%

“…However, it requires the construction of datasets containing a large number of labels, which is expensive and time-consuming. To alleviate the above problems, a multipath identification method based on unsupervised K-means [ 16 , 17 , 18 ] is proposed. However, this unsupervised machine learning (ML) method is less effective than supervised learning methods in terms of recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

Qin,

Li,

Xie

et al. 2024

Sensors

View full text Add to dashboard Cite

The BeiDou Navigation Satellite System (BDS) provides real-time absolute location services to users around the world and plays a key role in the rapidly evolving field of autonomous driving. In complex urban environments, the positioning accuracy of BDS often suffers from large deviations due to non-line-of-sight (NLOS) signals. Deep learning (DL) methods have shown strong capabilities in detecting complex and variable NLOS signals. However, these methods still suffer from the following limitations. On the one hand, supervised learning methods require labeled samples for learning, which inevitably encounters the bottleneck of difficulty in constructing databases with a large number of labels. On the other hand, the collected data tend to have varying degrees of noise, leading to low accuracy and poor generalization performance of the detection model, especially when the environment around the receiver changes. In this article, we propose a novel deep neural architecture named convolutional denoising autoencoder network (CDAENet) to detect NLOS in urban forest environments. Specifically, we first design a denoising autoencoder based on unsupervised DL to reduce the long time series signal dimension and extract the deep features of the data. Meanwhile, denoising autoencoders improve the model’s robustness in identifying noisy data by introducing a certain amount of noise into the input data. Then, an MLP algorithm is used to identify the non-linearity of the BDS signal. Finally, the performance of the proposed CDAENet model is validated on a real urban forest dataset. The experimental results show that the satellite detection accuracy of our proposed algorithm is more than 95%, which is about an 8% improvement over existing machine-learning-based methods and about 3% improvement over deep-learning-based approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

Qin,

Li,

Xie

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Jiang [19] proposes an unsupervised algorithm that detects indirect signals by utilizing a clustering method. GPS data in an offline system are categorized as normal or abnormal when an indirect signal reaches the receiver.…”

Section: Receiver-based Techniquesmentioning

confidence: 99%

Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

Ozdemir

Işık

Geragersian

et al. 2023

AIAA SCITECH 2023 Forum

View full text Add to dashboard Cite

As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.1 Graduate student, School of Aerospace, Transport and Manufacturing (SATM) 2 PhD candidate, School of Aerospace, Transport and Manufacturing (SATM) 3 Senior Lecturer, Centre for Autonomous and Cyberphysical Systems

show abstract

“…However, some recent works have attempted to explore the use of unsupervised multipath-signal-recognition methods. For example, the generative adversarial network considers unsupervised domain adaptation (UDA)based models [21] to reduce the discrepancy between real and simulated data. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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

Qin,

Li,

Xie

et al. 2025

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite System (BDS). However, most existing approaches to this issue involve supervised machine learning (ML) methods, and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal labeling. Inspired by an autoencoder with powerful unsupervised feature extraction, we propose a new deep learning (DL) model for BDS signal recognition that places a long short-term memory (LSTM) module in series with a convolutional sparse autoencoder to create a new autoencoder structure. First, we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time series.Second, we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data, which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series signals. Finally, we add an regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition accuracy. We tested our proposed approach on a real urban canyon dataset, and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods (e.g., 11% better than a support vector machine) and two existing DL-based methods (e.g., 7.26% better than convolutional neural networks).

show abstract

Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled System in Urban Areas

Cited by 15 publications

References 39 publications

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

An Efficient Convolutional Denoising Autoencoder-Based BDS NLOS Detection Method in Urban Forest Environments

Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

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

Contact Info

Product

Resources

About