Adjacent point correlativity mean noise reduction method implemented for FFT data

Abstract

The invention discloses an adjacent point correlativity mean noise reduction method implemented for FFT data. The method comprises the steps of setting a reference value dRef; comparing to-be-processed FFT signal data with the reference value dRef; for the data with the value lower than the reference value dRef, calculating a mean of a set number of pieces of adjacent data, and replacing the data value lower than the reference value dRef with the mean; and reducing the amplitude of distorted data with the value lower than the reference value and reducing the noise band to enable signals with values lower than the reference value to gather to the center of noise. The amplitude of the distorted data with the value lower than the reference value can be reduced through an adjacent data mean method and the noise band is reduced, so that the signals with the values lower than the reference value can gather to the center of the noise, and the noise can be inhibited and the interference signal energy can be reduced to a certain extent.

Description

technical field [0001] The invention relates to the field of digital signal processing, in particular to an adjacent point correlation mean noise reduction method for FFT data. Background technique [0002] In the field of high-speed acquisition signal analysis and processing, after the high-speed sampling data is transformed and processed by FFT, due to the difficulty of hardware design and real-time data analysis and processing under high-speed sampling conditions, it is often caused by large bandwidth problems, signal leakage problems, signal aliasing problems, band Internal self-excitation problems, etc., make the effect of FFT unsatisfactory. Some unwanted signals or small signals that are not concerned will be mixed on the signal noise floor. It is difficult to improve hardware and optimize programmable logic timing at high speeds. Solve and affect the data processing effect. [0003] Usually, when the hardware environment is not ideal, especially in high-speed applic...

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Problems solved by technology

[0002] In the field of high-speed acquisition signal analysis and processing, after the high-speed sampling data is transformed and processed by FFT, due to the difficulty of hardware design and real-time data analysis and processing under high-speed sampling conditions, it is often caused by large bandwidth problems, signal leakage problems, signal aliasing problems, band Internal self-excitation problems, etc., make the effect of FFT unsatisfactory. Some unwanted signals or small signals that are not concerned will be mixed on the signal noise floor. It is difficult to improve hardware and optimize programmable logic timing at high speeds. Solve, affect the effect of data processing

[0003] Usually, when the hardware environment is not ideal, especially in high-speed applications, it is generally difficult to completely improve the signal effect through hardware optimization.

At the same time, the improvement of the hardware environment needs to increase labor costs, time expansion, and hardware resource consumption to a certain extent, and in many cases, due to different environments, the results of one debugging do not meet the general requirements of multiple environments.

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