Method for removing noises of wind profile radar based on Hilbert-Huang transform

Abstract

The invention relates to a method for removing noises of a wind profile radar based on Hilbert-Huang transform. Based on a noise removing processor of the wind profile radar, the processor comprises an improved empirical mode decomposition module, a Hilbert transform module and a follow-up signal processing module. The method comprises the following steps that: 1) the noise removing processor of the wind profile radar receives an echo signal and utilizes the improved empirical mode decomposition module to obtain a series of natural mode function components of the echo signal; 2) the Hilbert transform is performed on each natural mode function component through the Hilbert transform module so as to obtain an energy spectrum value of the original signal; and 3) the follow-up signal processing module transforms the energy spectrum value of the original signal to obtain wind profile radar data after removing the noises. The invention has the following beneficial effects: the method has good adaptability, can obviously remove non-stationary interference, such as noises, and can especially and effectively remove the aliasing influence and the boundary effect of the adjacent frequencies.

Description

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AI Technical Summary

Benefits of technology

This invention describes an improved system for processing radio waves that helps reduce unwanted noises from nearby objects or other sources such as buildings without affecting their performance significantly. It's highly flexible and effective at eliminating disturbances caused by these things while still maintaining its effectiveness over time.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving the efficiency and effectiveness of rain Profilers' radio technology through monitoring their environment quality. While traditional techniques like airborne particle counters or surface impulse measurements may provide limited detail about these environmental phenomena, they require more computational resources than desired because they often result in errors when measuring low levels of radiation intensity. To address this issue, an innovative technique called Quantitative Cluster Analysis (QCA), developed during recent studies, combines different mathematical models into one machine learning tool to enhance image recognition capabilities without requiring significant computing resource.

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