Average noise reducing method implemented on FFT (Fast Fourier Transform) data

Abstract

The invention discloses an average noise reducing method implemented on FFT (Fast Fourier Transform) data. The method comprises the following steps: averaging FFT signal data within a set range and data lower than a reference value dRef to obtain an average noise baseline value lower than the reference value dRef; then, averaging data lower than the reference value dRef in data to be processed together with the average noise baseline value; and drawing signals below the set reference value close to the average noise baseline value in order to restrain noise and reduce interference signal energy. Through adoption of the method, the requirements and dependencies on hardware are simplified greatly, and overhead caused by hardware debugging is reduced at the same time.

Description

technical field [0001] The invention relates to the technical field of digital signal processing, in particular to an average 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 processed by FFT, due to the difficulty of hardware design and real-time data analysis and processing under high-sampling conditions, it is often caused by large bandwidth problems, signal leakage problems, signal aliasing problems, Internal self-excitation problems, etc., make the effect of FFT unsatisfactory. Some useless signals or small signals that are not concerned will be mixed on the signal noise floor. It is difficult to solve it by improving hardware and optimizing programmable logic timing at high speed. affect data processing. [0003] Usually, when the hardware environment is not ideal, especially in high-speed applications, it is generally difficult to completely ...

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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 processed by FFT, due to the difficulty of hardware design and real-time data analysis and processing under high-sampling conditions, it is often caused by large bandwidth problems, signal leakage problems, signal aliasing problems, Internal self-excitation problems, etc., make the effect of FFT unsatisfactory. Some useless signals or small signals that are not concerned will be mixed on the signal noise floor. It is difficult to solve it by improving hardware and optimizing programmable logic timing at high speed. 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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