Self-adaptive resampling method based on window function design

Abstract

The invention discloses an adaptive resampling method based on window function design. The adaptive resampling method comprises the following steps: firstly, establishing a universal resampling modelbased on a Farrow structure; establishing a fractional delay filter design model based on a window function method, and obtaining a plurality of groups of fractional delay filters by utilizing the model; constructing a fractional delay filter matrix on the basis of the obtained multiple groups of fractional delay filters; solving a coefficient matrix C of the Farrow structure sub-filter by using aleast square method; and substituting the obtained Farrow structure sub-filter coefficient matrix C into the resampling model established in the step 1 to complete the establishment of a universal Farrow structure resampling model. The method is better in flexibility and smaller in calculated amount, a proper model can be obtained by adjusting parameters for multiple times, sampling at any pointis achieved, and the function of arbitrary conversion between sampling rates is achieved.

Description

technical field [0001] The invention relates to the technical field of digital signal processing, in particular to an adaptive resampling method based on window function design. Background technique [0002] Resampling technology is widely used in signal processing, audio, communication and other fields because it can convert any sampling rate and sample any point. The key to realizing resampling technology is to design a variable fractional delay digital filter for interpolation. At present, Commonly used methods for designing variable fractional delay filters are based on polyphase filters, based on cascaded integral comb filters (CIC) and based on Farrow structure finite impulse response delay filters. In these methods, the finite impulse response based on Farrow structure Impulse response delay filter design method stands out due to its flexible and adjustable Farrow structure, efficient calculation in practical applications, and low resource utilization, and has become ...

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Problems solved by technology

Among them, the particle swarm optimization algorithm is only suitable for the case where the coefficient dimension is low. When the coefficient dimension is high, the algorithm cannot be further optimized; while the minimax method and the weighted least squares method are both aimed at minimizing the design error. Design matrix inversion operations increase the amount of calculation in the design process, so there are defects such as long design time and complicated adjustment of weight parameters.

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