Wave Height Estimation From X-Band Radar Data Using Variational Mode Decomposition

Yang, Zhiding; Huang, Weimin

doi:10.1109/lgrs.2022.3195675

Cited by 8 publications

(5 citation statements)

References 26 publications

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“…This signal comprises the pulse wave, respiratory, and heartbeat signals, along with their corresponding harmonic waves and slight movements interferences. VMD can decompose the mixed signal into several modal signals with different frequencies [ 21 ], allowing for the separation of the pulse wave from these modal signals. The objective is to solve the following constrained variational problem:

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Section: The Proposed Trccbp Methodsmentioning

confidence: 99%

TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring

Jiang,

Zhang,

et al. 2023

Sensors

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Contactless continuous blood pressure (BP) monitoring is of great significance for daily healthcare. Radar-based continuous monitoring methods typically extract time-domain features manually such as pulse transit time (PTT) to calculate the BP. However, breathing and slight body movements usually distort the features extracted from pulse-wave signals, especially in long-term continuous monitoring, and manually extracted features may have limited performance for BP estimation. This article proposes a Transformer network for Radar-based Contactless Continuous Blood Pressure monitoring (TRCCBP). A heartbeat signal-guided single-beat pulse wave extraction method is designed to obtain pure pulse-wave signals. A transformer network-based blood pressure estimation network is proposed to estimate BP, which utilizes convolutional layers with different scales, a gated recurrent unit (GRU) to capture time-dependence in continuous radar signal and multi-head attention modules to capture deep temporal domain characteristics. A radar signal dataset captured in an indoor environment containing 31 persons and a real medical situation containing five persons is set up to evaluate the performance of TRCCBP. Compared with the state-of-the-art method, the average accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP) is 4.49 mmHg and 4.73 mmHg, improved by 12.36 mmHg and 8.80 mmHg, respectively. The proposed TRCCBP source codes and radar signal dataset have been made open-source online for further research.

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…”

Section: The Proposed Trccbp Methodsmentioning

confidence: 99%

TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring

Jiang,

Zhang,

et al. 2023

Sensors

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“…In addition to SNR-based methods, there are some alternative methods that have been proposed to estimate wave parameters, such as empirical orthogonal function-based methods [14], iterative least squares-based methods [1], 2D continuous wavelet transform-based methods [15], array-beamforming-based methods [16], shadowing mitigation-based methods [17], and synchrosqueezed wavelet transform-based methods [18]. Additionally, there are some other methods for estimating H s that have been proposed, such as shadowing-based methods [19,20], ensemble empirical mode decomposition-based methods [21], correlation analysis-based methods [22], and variational mode decomposition-based methods [23]. It is worth noting that machine learning algorithms have been applied to H s estimation, which can simplify the cumbersome steps of previous algorithms and improve computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Wave Height and Period Estimation from X-Band Marine Radar Images Using Convolutional Neural Network

Zuo,

Wang,

Wang

et al. 2024

JMSE

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In this study, a deep learning network for extracting spatial-temporal features is proposed to estimate significant wave height (Hs) and wave period (Ts) from X-band marine radar images. Since the shore-based radar image in this study is interfered with by other radar radial noise lines and solid target objects, to ensure that the proposed convolutional neural network (CNN) extracts the image features accurately, it is necessary to pre-process the radar image to eliminate interference. Firstly, a pre-trained GoogLeNet is used to extract multi-scale depth space features from the radar images to estimate Hs and Ts. Since CNN-based models cannot analyze the temporal behavior of wave features in radar image sequences, self-attention is connected after the deep convolutional layer of the CNN to construct a convolutional self-attention (CNNSA)-based model that generates spatial-temporal features for Hs and Ts estimation. Simultaneously, Hs and Ts measured by nearby buoys are used for model training and reference. The experimental results show that the proposed CNNSA model reduces the RMSD by 0.24 m and 0.11 m, respectively, in Hs estimation compared to the traditional SNR-based and CNN-based methods. In Ts estimation, the RMSD is reduced by 0.3 s and 0.08 s, respectively.

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“…Unlike the spatial-temporal dimension approach, Zhao et al [13] recently proposed a method to retrieve ocean wave parameters only from the time Doppler spectrum collected with the coherent S-band radar, which can retrieve ocean wave parameters without spatial observations. Yang et al [24] obtained 10 intrinsic mode functions (IMFs) by variational mode decomposition (VMD) of X-band radar sub-images, and obtained reasonable wave height by linear fitting using the sum of amplitude modulation (AM) components extracted from the sixth to the ninth IMF components, but did not verify the wave period parameters.…”

Section: Introductionmentioning

confidence: 99%

Inversion of Wave Parameters With Shore-Based Coherent $S$-Band Radar Using Quasi-Binary Variational Mode Decomposition

Hu,

Chen,

Zhao

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Coherent microwave radar utilizes the direct relationship between the orbital wave velocity and the wave height spectrum to retrieve wave parameters. However, due to the broken wave and its evolution, the "Group-Line" is introduced in the radar-obtained wavenumber-frequency spectrum, and the dominant wave energy is reduced. Many methods have proposed to remove the "Group-Line" in the wavenumber-frequency spectrum, but these methods can only remove low-frequency energy and fail to compensate for the dominant wave energy. To solve this problem, a method for wave parameter inversion using the quasibinary variational mode decomposition (QB-VMD) is proposed. Firstly, the QB-VMD decomposes the spatial-temporal radial velocity series, and a series of modes are obtained, including the "Group-Line" and dominant wave modes. Secondly, the "Group-Line" mode is discarded, and the dominant wave mode is compensated appropriately to reconstruct the spatial-temporal radial velocity series. Finally, the wave parameters are retrieved according to the reconstructed spatial-temporal radial velocity series. The proposed method is verified by simulation. In addition, this paper uses on 8-day data set collected by the coherent Sband radar deployed at the Beishuang island for analysis. The significant wave height and mean wave period are retrieved from the data set. The wave parameters estimated by the proposed method are compared with the buoy results. The correlation coefficients are 0.97 and 0.80, and the root means square errors are 0.12 m and 0.49 s, respectively. The results show that the proposed method can invert wave parameters using the coherent S-band radar with a reasonable performance.

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Wave Height Estimation From X-Band Radar Data Using Variational Mode Decomposition

Cited by 8 publications

References 26 publications

TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring

TRCCBP: Transformer Network for Radar-Based Contactless Continuous Blood Pressure Monitoring

Wave Height and Period Estimation from X-Band Marine Radar Images Using Convolutional Neural Network

Inversion of Wave Parameters With Shore-Based Coherent $S$-Band Radar Using Quasi-Binary Variational Mode Decomposition

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