Filter boundary frequency band control method and device based on genetic algorithm

Abstract

The invention discloses a filter boundary frequency band control method and device based on a genetic algorithm. The method comprises the steps that the filter frequency sampling vector length, boundary frequency band parameters and horizontal moving amount parameters are obtained according to the passband cut-off frequency of a filter and the bandwidth of a transitional band of the filter; the coefficient of a filter to be compensated with the length of 2N-1 is obtained accordingly; the optimist Caesar parameter obtained through operation of the genetic algorithm is substituted into a coefficient analytic expression of a compensation filter to obtain the coefficient of the compensation filter after the optimist Caesar convolution window is optimized; the finally needed filter coefficient is obtained by adding the coefficient of the filter to be compensated to the coefficient of the compensation filter. The device comprises an outer RAM, a DSP and an output driving and displaying circuit. Firstly, the needed passband cut-off frequency of the filter and the bandwidth of the transitional band of the filter are stored into the outer RAM, and then are input into the DSP in real time, and the needed filter coefficient and the transmission curve are obtained finally through processing of internal core algorithms and the like of the DSP and are displayed in real time through the output driver and display circuit.

Description

technical field [0001] The invention relates to the technical field of digital signal processing, in particular to a genetic algorithm-based filter boundary frequency band control method and a device thereof. Background technique [0002] In the design of finite impulse response filter (Finite Impulse Response Filter, FIR filter), it has always been a technology to take into account the good transmission performance of the filter (that is, to ensure that the passband ripple is small enough and the stopband attenuation is large enough) and the filter boundary frequency band is precisely controlled. problem. This problem is prominent in both classical filter design methods and modern filter design methods. [0003] Classical filter design methods, such as the window function method, can set the boundary frequency band parameter ω c Substituting directly into the ideal filter formula to obtain the filter coefficients, but since the ideal filter coefficients are infinitely lon...

AI Technical Summary

Problems solved by technology

At present, the literature [13] has used the genetic algorithm in the FIR digital filter design - the frequency sampling method to determine the optimal value of the transition zone samples, which solves the problem that the traditional look-up table method cannot guarantee the optimal data

However, the genetic algorithm in [13] can only control the boundary frequency band at the N frequency sampling points of the frequency sampling vector with length N

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