Digital slide average low-pass filter and filtering method thereof

Abstract

The invention relates to a digital slide average low-pass filter and a filtering method thereof. The filter comprises a digital filter. The digital filter comprises an A / D converting unit, a first calculation unit, a second calculation unit, a first storage unit and a second storage unit. The A / D converting unit converses a sampling analog signal into a digital signal and send the digital signal to the first calculation unit. The first storage unit stores data cumulative sum output by the first calculation unit. The second storage unit stores a digital filter output value output by the first calculation unit. The first calculation unit reads the data of the first storage unit and the second storage unit and calculates the data cumulative value of the current cycle. The second calculation unit receives the data sent by the first calculation unit and updates and stores the digital filter output value to the second storage unit. According to the invention, a traditional filtering method heavily relying on the memory space of a chip is optimized to the filtering method simply needing two storage units, so that the relying degree of the method on system resources is greatly reduced.

Description

technical field [0001] The invention relates to the technical field of digital filters and filtering methods, in particular to a digital moving average low-pass filter and a filtering method thereof. Background technique [0002] The moving average filter is a low-pass filter, which is mainly used to smooth the data and filter out the high-frequency components in the signal. It completes the operation by averaging a certain number of input signals to obtain the output signal of each point. . In UPS design, aiming at parameters such as bus voltage and battery, moving average filtering algorithm is often used to remove sampling error and interference and smooth the output. [0003] The calculation method of the traditional moving average filter is to calculate the average value of multiple consecutive m-item sequences from the sampling value from a time series with n items. However, since the traditional sliding translation filter needs to continuously release the sampling h...

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Problems solved by technology

However, since the traditional sliding translation filter needs to continuously release the sampling history value, it is necessary to store the sampling history value, and the number of historical data needs to be stored as much as the filtering depth, so it takes up a lot of chip memory space, as shown in the attached figure 1 shown

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