Method for modeling sea wave significant wave height inversion model based on particle swarm optimization (PSO) self-adaptive piecewise linear fitting

Abstract

The invention provides a method for modeling a sea wave significant wave height inversion model based on particle swarm optimization (PSO) self-adaptive piecewise linear fitting, and belongs to the technical field of sea wave parameter inversion. The method comprises the steps of performing outlier removal processing on data, performing sparsification processing on the data, initializing parameters in a particle swarm, initializing a particle speed, updating the particle speed, updating particle displacement and the like. According to the method for modeling the sea wave significant wave height inversion model based on self-adaptive piecewise linear fitting, the wave height is subjected to inversion by utilizing a particle swarm optimization, so that the function of a conventional algorithm can be realized, the precision of the conventional algorithm is achieved, and more precise wave height inversion can be performed; and moreover, when the number of the pieces in the method is more than or equal to two, the modeling precision is higher than that of a conventional modeling method. The inversion model modeled by the method has higher inversion precision compared with the inversion model modeled by the conventional method, and moreover, the method for modeling a sea wave significant wave height inversion model based on PSO self-adaptive piecewise linear fitting is wide in applicability and high in flexibility.

Description

technical field [0001] The invention belongs to the technical field of sea wave parameter inversion, and in particular relates to a modeling method for sea wave effective wave height inversion model based on PSO adaptive piecewise linear fitting. Background technique [0002] Ocean waves are the ocean phenomenon most directly and closely related to human beings. Factors such as wave height, wave direction, and wave cycle are of great significance to the safety of shipping, ports, and offshore oil platforms. Sea clutter images formed by marine X-band navigation radar echoes contain rich ocean wave information, and the ocean wave spectrum and wave parameters can be retrieved by using the echo intensity of the radar. In 1985, Young et al. proposed for the first time the method of extracting ocean wave information from the "sea clutter" radar image sequence. Once the method was discovered, it aroused great interest. In the following 10 years, Zimer, Rosenthal, Günther and othe...

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Problems solved by technology

The method disclosed in the present invention differs from the existing methods in that it is considered that the effective wave height of the ocean wave increases with the increase of the square root of the signal-to-noise ratio of the radar image, but the relationship between the two is not completely linear at different wave height stages, so The existing method uses the least squares method to fit the square root of the SNR and the wave height piecewise linearly to represent the relationship between the square root of the SNR and the wave height, but the problem brought by the piecewise linearization is that the segment position How to determine the determination of each line segment and how to connect each line segment, using the multi-dimensional space search function of the particle swarm optimization algorithm, can adaptively solve the problems that cannot be completed by least squares, the determination of the segment position and the connection of each segment, etc., and then improve Accuracy of Significant Wave Height Retrieval Using Radar Image Signal-to-Noise Ratio Square Root

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