Deep Learning-Aided Spatial Discrimination for Multipath Mitigation

Abdallah, Ali A.; Kassas, Zaher M.

doi:10.1109/plans46316.2020.9109935

Cited by 23 publications

(8 citation statements)

References 25 publications

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“…Ref. [ 33 ] developed a deep neural network (DNN) to address the limitations of traditional beamforming methods and improve the positioning performance of vehicle navigation in harsh multipath environments. Meanwhile, a model for NLOS signal detection and correction based on CNN and variational modal decomposition is proposed.…”

Section: Related Workmentioning

confidence: 99%

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

Qin,

Li,

Xie

et al. 2024

Sensors

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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.

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Section: Related Workmentioning

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

“…For instance, ref. [ 26 ] presented a deep-learning-based beamforming approach to mitigate multipath. That work highlighted the limitations of conventional beamforming algorithms by developing a DNN-based model and applied it in different environments, showing a root mean-squared error (RMSE) reduction.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning of GNSS Acquisition

Borhani-Darian

et al. 2023

Sensors

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Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios.

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“…It can be integrated with the fuzzy logic principle to achieve better accuracy on multipath or NLOS detection [41]. The neural network architecture can even extract representations from the GNSS correlator-level data to estimate parameters for multipath mitigation [42], or directly output the multipath-mitigated measurements [43]. This approach is further extended by integrating the CNN to conduct multipath detection and mitigation [44], or substitute the conventional correlation process in a GNSS receiver [45].…”

Section: Introductionmentioning

confidence: 99%

Prediction on the Urban GNSS Measurement Uncertainty Based on Deep Learning Networks With Long Short-Term Memory

Zhang

et al. 2021

IEEE Sensors J.

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The GNSS performance could be significantly degraded by the interferences in an urban canyon, such as the blockage of the direct signal and the measurement error due to reflected signals. Such interferences can hardly be predicted by statistical or physical models, making urban GNSS positioning unable to achieve satisfactory accuracy. The deep learning networks, specializing in extracting abstract representations from data, may learn the representation about the GNSS measurement quality from existing measurements, which can be employed to predict the interferences in an urban area. In this study, we proposed a deep learning network architecture combining the conventional fully connected neural networks (FCNNs) and the long short-term memory (LSTM) networks, to predict the GNSS satellite visibility and pseudorange error based on GNSS measurement-level data. The performance of the proposed deep learning networks is evaluated by real experimental data in an urban area. It can predict the satellite visibility with 80.1% accuracy and predict the pseudorange errors with an average difference of 4.9 meters to the labeled errors. Experiments are conducted to investigate what representations have been learned from data by the proposed deep learning networks. Analysis results show that the LSTM layer within the proposed networks may contain representations about the environment, which affects the prediction behavior and can associate with the real environment information.

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Deep Learning-Aided Spatial Discrimination for Multipath Mitigation

Cited by 23 publications

References 25 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

Deep Learning of GNSS Acquisition

Prediction on the Urban GNSS Measurement Uncertainty Based on Deep Learning Networks With Long Short-Term Memory

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