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The sea clutter model based on the physical sea surface can simulate radar echo at different times and positions and is more suitable for describing dynamic sea clutter than the traditional models based on statistical significance. However, when applying the physical surface model to shore-based radar, the effects of wave nonlinearity, breaking wav...
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Context 1
... pointed out that the wave steepness limit is achieved when the horizontal trajectory velocity of the water particle on the wave crest is exactly equal to the wave velocity. At this time, the wave crest is steep and unstable, and the wave crest angle is equal to 120 • [38] (see Figure 3). ...
Context 2
... pointed out that the wave steepness limit is achieved when the horizontal trajectory velocity of the water particle on the wave crest is exactly equal to the wave velocity. At this time, the wave crest is steep and unstable, and the wave crest angle is equal to 120° [38] (see Figure 3). Based on the limit crest angle, Michell [39] proposed that the wave steepness limit for deep-water propulsion waves is: ...
Citations
... The statistical properties of sea clutter play a significant role in determining the constant false alarm characteristics of target detection [1][2][3]. Specifically, the collected sea clutter data from shore-based radar exhibits a pronounced long trailing behavior in its statistical distribution [4][5][6][7][8], commonly referred to as the phenomenon of heavy trailing. The composite Gaussian model is well-suited for capturing the heavy trailing characteristics observed in sea clutter data [9][10][11]. ...
Accurate parameter estimation is essential for modeling the statistical characteristics of ocean clutter. Common parameter estimation methods in generalized Pareto distribution models have limitations, such as restricted parameter ranges, lack of closed-form expressions, and low estimation accuracy. In this study, the particle swarm optimization (PSO) algorithm is used to solve the non-closed-form parameter estimation equations of the generalized Pareto distribution. The goodness-of-fit experiments show that the PSO algorithm effectively solves the non-closed parameter estimation problem and enhances the robustness of fitting the generalized Pareto distribution to heavy-tailed oceanic clutter data. In addition, a new parameter estimation method for the generalized Pareto distribution is proposed in this study. By using the difference between the statistical histogram of the data and the probability density function/cumulative distribution function of the generalized Pareto distribution as the target, an adaptive function with weighted coefficients is constructed to estimate the distribution parameters. A hybrid PSO (HPSO) algorithm is used to search for the best position of the fitness function to achieve the best parameter estimation of the generalized Pareto distribution. Simulation analysis shows that the HPSO algorithm outperforms the PSO algorithm in solving the parameter optimization task of the generalized Pareto distribution. A comparison with other traditional parameter estimation methods for generalized Pareto distribution shows that the HPSOHPSO algorithm exhibits strong parameter estimation performance, is efficient and stable, and is not limited by the parameter range.
An ocean water bathymetry method for analyzing sea clutter time series from shore-based microwave radar is proposed. Based on the relationship between wave phase velocity and water depth, a quantitative model is established for the breaking wave Doppler velocity, peak period, and depth. This method combines a short-time Fourier transform technique for retrieving breaking wave velocity by evaluating the high-speed echoes, and the maximal overlap discrete wavelet transform for retrieving the corresponding wave peak period. Compared with conventional radar image-based methods, the main advantages of this approach are as follows: a) the time-frequency analysis-based and wavelet-based approaches are localized, naturally fitting the typical inhomogeneous conditions observed in nearshore areas; b) the method is dynamic, and the time-evolving model satisfies the instability condition of the sea surface state; c) it does not rely on the wavenumber-frequency spectrum to fit the dispersion curve, thus avoiding the influence of surface current; d) it utilizes a single azimuth time series without the need for continuous radar images. The validity of the method is confirmed through experimentation with actual radar sea clutter. The bathymetry results of the proposed approach demonstrate consistency and robustness under different conditions of sea clutter.
Coherent microwave radar is one of the effective devices for ocean wave measurements. However, the accuracy of wave inversion is significantly affected by the non-gravity wave components (referred to as non-wave components in this work) such as broken waves and shadows in the sea echoes. In this article, a new method based on the Radon transform and discrete Fourier transform (DFT) is proposed to suppress the non-wave components. First, the spatial-temporal velocities are calculated based on the raw data obtained from coherent microwave radar. Second, the Radon transform and DFT are performed on the spatial-temporal velocities to obtain the distribution of the spatial-temporal velocities in the Radon-Fourier transform domain. Following this, non-wave components are eliminated in accordance with the phase velocity and frequency distribution of the wave components. To validate the method, we analyzed the experimental data collected with a coherent S-band radar in Weihai in December 2021. After removing the non-wave components from the radar echoes, we invert three wave parameters including peak wave period, peak wave direction, and significant wave height, and compare them with the
in-situ
data. The results indicate that the method can effectively remove the non-wave components from the wave echoes without any empirical parameters.
