Simplified weighted repeat pseudo-median filtering method with negative coefficients

Abstract

The invention discloses a simplified weighted repeat pseudo-median filtering method with negative coefficients. The method comprises the following steps: taking the frequency response characteristics of an approximation same-order finite impulse response (FIR) filter as an objective, designing weighting coefficients containing negative numbers for weighted pseudo-median computation of filtering window data by an MALLOWS comprehensive method, adjusting the window data by the weighting coefficients; simultaneously, computing weighting coefficients based on the slopes of a group of special structures designed for the filter, computing the overall slope of the window data repeatedly by the weighted pseudo median, carrying out linear transformation on the window data by the slope; and finally carrying out weighted computation on the transformed data by the pseudo-median weighting coefficients to obtain the output value of weighted repeat pseudo-median filtering. The method realizes a better frequency selection characteristic than that of the existing negative coefficient weighted median or pseudo-median filtering method, and simplifies computation in the algorithm to meet the requirement of systems with finite resources, such as a wireless sensor and the like for low power consumption.

Description

technical field [0001] The invention relates to a simplified weighted repeated pseudo-median filtering method with negative coefficients. Background technique [0002] Median filtering is a very simple and practical nonlinear filtering method, which can not only remove or reduce random noise and pulse interference, but also effectively retain the edge change information of the signal. The current improved methods of median filtering are mainly aimed at two-dimensional image signal processing. For non-image signal processing such as vibration signals, biomedical signals, and voice signals that need to meet certain real-time requirements, because they have more performance (such as frequency band, speed, power consumption, etc.) for nonlinear filtering techniques such as median filtering ) requirements are significantly different from those for image signal processing, especially for low-power microcontrollers with limited hardware resources (such as processing power, storage...

AI Technical Summary

Problems solved by technology

Other methods with relatively good performance are computationally complex and are not suitable for application on such low-end hardware platforms as used by WSN nodes.

However, there is still a lack of universal filtering methods for signal processing on low-end hardware platforms such as WSN nodes.

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