Code multipath error extraction based on the wavelet and empirical mode decomposition for Android smart devices

Li, Wanqing; Chen, Zhengkun; Dai, Zhiqiang; Li, Junzhi; Ran, Chengxin

doi:10.1007/s10291-021-01122-1

Cited by 13 publications

(3 citation statements)

References 25 publications

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“…As a bridge between the time domain and frequency domain, Fourier transform plays an extremely important role in early signal analysis and processing. 25 Wavelet transform can obtain not only the frequency component of the signal but also the occurrence time of each frequency signal. From a mathematical point of view, the wavelet transform is composed of a set of wavelet basis functions, which can be obtained by the translation and scaling of the wavelet basis functions.…”

Section: Methodsmentioning

confidence: 99%

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Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

Shi

Wen

et al. 2022

ACS Omega

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Failure to blow ash on the heated surface of the boiler will cause a drop in heat transfer rate and even industrial safety accidents. Nowadays, the shortcomings of the fixed soot blowing operation every hour and every shift are significant, which can be improved by high-precision ash accumulation prediction. Therefore, this paper proposes a deep learning model fused with deep feature extraction. First, a dynamic fouling model and a health index-clearness factor ( CF ) of the heated surface are established. The data preprocessing method reduces unnecessary forecasting difficulty and makes the degradation trend of the CF time series more obvious. In addition, deep feature extraction is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and kernel principal component analysis (KPCA), which completes the multiscale analysis of time series and reduces the training time of deep learning models, and has significant contributions to improving prediction accuracy and reducing time consumption. The adaptive sliding window and the encoder–decoder based on the attention mechanism (EDA) can better mine the internal information of the time series. Compared with long short-term memory (LSTM), taking the 300 MW boiler’s various heated surface data sets as an example, multistep forward prediction and different starting point prediction experiments have verified the superiority and effectiveness of the model. Finally, under the variable working condition economizer datasets, the proposed method better completes the predictive maintenance task of the heated surface. The research results provide operational guidance for improving heat transfer rate, energy saving, and reducing consumption.

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Section: Methodsmentioning

confidence: 99%

“…As a bridge between the time domain and frequency domain, Fourier transform plays an extremely important role in early signal analysis and processing . Wavelet transform can obtain not only the frequency component of the signal but also the occurrence time of each frequency signal.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

Shi

Wen

et al. 2022

ACS Omega

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“…In 2019, Zhang et al applied EMD to the monitoring of tide variation and enhanced considerably the utilization rate of GNSS data [18]. In 2021, Li et al combined the waveletbased and EMD to reduce the noise of code minus carrier (CMC) and improve positioning performance [19]. In 2022, Tong proposed a method for extracting GPS/BDS-3 multipath errors based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and WT methods [20].…”

Section: Introductionmentioning

confidence: 99%

An adaptive frequency-based C/N₀-constrained multipath extraction and mitigation method for GNSS pseudorange measurements

Liu,

Li,

Huang

et al. 2024

Meas. Sci. Technol.

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The multipath effect poses an inevitable challenge for Global Navigation Satellite System(GNSS) receivers, particularly pronounced in smart devices, which are nevertheless the most common means through which the general public accesses GNSS positioning. So we propose a novel adaptive method for extracting multipath errors, which fully exploits the multipath frequency characteristics, multipath repeatability, and the correlation between multipath errors and carrier to- -noise ratio (C/N0). This approach aims to enhance the susceptibility of smart devices to multipath influences. Specifically, this method uses the code minus carrier (CMC) method to calculate the noisy multipath error,multipath to constrain the frequency range,exploits the correlation between C/N0 andemploys the ensemble empirical modedecomposition (EEMD) method to decompose the error signal, and utilizes the Hilbert–Huangtransform (HHT) method to obtain the frequency information of the signal component.multipath Inrepeatability, the proposed method can effectively improve the correlation ofmultipath signals by up to 0.79. By applying the extraction method, the positioningperformance of smart devices can be improved by 13.9%. Therefore, the proposed method extracts multipath error more accurately and is highly usable in low-cost devices and real-time applications.

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Multipath mitigation in GNSS precise point positioning using multipath hierarchy for changing environments

Yuan,

Zhang,

et al. 2023

GPS Solut

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Code multipath error extraction based on the wavelet and empirical mode decomposition for Android smart devices

Cited by 13 publications

References 25 publications

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

An adaptive frequency-based C/N₀-constrained multipath extraction and mitigation method for GNSS pseudorange measurements

Multipath mitigation in GNSS precise point positioning using multipath hierarchy for changing environments

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Code multipath error extraction based on the wavelet and empirical mode decomposition for Android smart devices

Cited by 13 publications

References 25 publications

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

Deep Learning-Based Approach for Heat Transfer Efficiency Prediction with Deep Feature Extraction

An adaptive frequency-based C/N0-constrained multipath extraction and mitigation method for GNSS pseudorange measurements

Multipath mitigation in GNSS precise point positioning using multipath hierarchy for changing environments

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Product

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An adaptive frequency-based C/N₀-constrained multipath extraction and mitigation method for GNSS pseudorange measurements