A Method for Retrieving Wave Parameters From Synthetic X-Band Marine Radar Images

Wei, Yanbo; Yan, Zheng; Lu, Zhizhong

doi:10.1109/access.2020.3037157

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…LeBlond and Mysak found in the study of hydrodynamic characteristics of wave that the gravity wave of sea wave conformed to the dispersion relationship in the linear wave theory. LeBlond et al assumed that the wave field and current field on the sea surface are spatially uniform and temporally stable in the selected region, and the first-order gravity wave of the sea wave satisfies the following dispersion relationship [ 65 , 66 , 67 ]:

where

is the frequency of sea gravity wave, g is the gravity acceleration, h is the water depth and k is the modulu of wave number.…”

Section: Sea Clutter Model Of Marine Radar Image Sequencementioning

confidence: 99%

“…Inspired by the wave parameter inversion theory of X-band marine radar [ 65 , 66 , 67 ], aiming at the problem that the existing target detection methods under sea clutter suppression are not applicable to non-coherent marine radars, this paper proposes a sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering. In this method, firstly, sea clutter energy in the image sequence is filtered by using the spatio-temporal domain joint sea clutter suppressor.…”

Section: Introductionmentioning

confidence: 99%

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Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

Wen

Wei

2022

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In marine radar target detection, sea clutter will cause a large number of missed alarms and false alarms, which will affect the accuracy of target detection. In order to suppress sea clutter effectively, a sea clutter suppression and target detection algorithm of marine radar image sequence based on spatio-temporal domain joint filtering is proposed in this paper. The proposed method is to add a sea clutter suppression link before detecting the target. Firstly, the marine radar image sequence is transformed into three-dimensional frequency wavenumber domain by three-dimensional fast Fourier transform (3D-FFT), and then the three-dimensional image spectrum is obtained. According to the fact that the sea clutter spectrum obtained from the image spectrum satisfies the dispersion relation of linear wave theory in the three-dimensional frequency wavenumber domain, a sea clutter model is established. Then, through the established sea clutter model, a spatio-temporal domain joint sea clutter suppressor is designed to filter the image spectrum. After that, the filtered image spectrum is transformed by three-dimensional inverse fast Fourier transform (3D-IFFT) to obtain the image sequence in which sea clutter is suppressed. Finally, target detection is carried out for sea clutter suppressed image sequence. The method is validated by using the real data of X-band marine radar. Compared with the classical Empirical mode decomposition (EMD) method, the improvement of signal-to-noise ratio (SNR) is more obvious, and SNR can be increased by 15.3 db at most. In addition, compared with target detection on original images directly, the proposed method has excellent detection rate and can increase detection rates by at least 8%.

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where

is the frequency of sea gravity wave, g is the gravity acceleration, h is the water depth and k is the modulu of wave number.…”

Section: Sea Clutter Model Of Marine Radar Image Sequencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

Wen

Wei

2022

Entropy

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“…Later studies have found that H s is not exactly linearly proportional to the square root of the SNR due to variations in sea states, different methods of calculating SNR, and differences in radar systems. 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].…”

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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“…In order to overcome the nonstationary and inhomogeneous characteristics of sea waves, the continuous wavelet transform [17,18] and the synchrosqueezed wavelet transform [19] are used to extract the wave spectra and parameters from a single radar image. Although the wavelet transform can enhance the retrieving accuracy of an SWH, it consumes much computing time, and 180 • direction ambiguity exists.…”

Section: Introductionmentioning

confidence: 99%

A Method of Extracting the SWH Based on a Constituted Wave Slope Feature Vector (WSFV) from X-Band Marine Radar Images

Wei,

Wang,

et al. 2023

Remote Sensing

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The shadow statistical method (SSM) used for extracting the significant wave height (SWH) from X-band marine radar images was further investigated because of its advantage of not requiring an external reference for calibration. Currently, a fixed shadow segmentation threshold is utilized to extract the SWH from a radar image based on the SSM. However, the retrieval accuracy of the SWH is not ideal for low wind speeds since the echo intensity of sea waves rapidly decays over distance. In order to solve this problem, an adaptive shadow threshold, which varies with echo intensity over distance and can accurately divide the radar image into shadow and nonshadow areas, is adopted to calculate the wave slope (WS) based on the texture feature of the edge image. Instead of using the averaged WS, the wave slope feature vector (WSFV) is constructed for retrieving the SWH since the illumination ratio and the calculated WS in the azimuth are different for shore-based radar images. In this paper, the SWH is calculated based on the constructed WSFV and classical support vector regression (SVR) technology. The collected 222 sets of X-band marine radar images with an SWH range of 1.0∼3.5 m and an average wind speed range of 5∼10 m/s were utilized to verify the performance of the proposed approach. The buoy record, which was deployed during the experiment, was used as the ground truth. For the proposed approach, the mean bias (BIAS) and the mean absolute error (MAE) were 0.03 m and 0.14 m when the ratio of the training set to the test set was 1:1. Compared to the traditional SSM, the correlation coefficient (CC) of the proposed approach increased by 0.27, and the root mean square error (RMSE) decreased by 0.28 m.

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A Method for Retrieving Wave Parameters From Synthetic X-Band Marine Radar Images

Cited by 7 publications

References 31 publications

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

Sea Clutter Suppression and Target Detection Algorithm of Marine Radar Image Sequence Based on Spatio-Temporal Domain Joint Filtering

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

A Method of Extracting the SWH Based on a Constituted Wave Slope Feature Vector (WSFV) from X-Band Marine Radar Images

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