Method for designing finite impulse response digital low-pass filter

Abstract

The invention belongs to the field of digital signal processing, and discloses a method for designing a finite impulse response digital low-pass filter. The method comprises the following steps: setting a frequency response function of an ideal low-pass filter; setting a frequency response function of an analog low-pass filter; setting a fitness function of a particle swarm algorithm; obtaining a position matrix of initial population particles and a velocity matrix of the initial population particles; obtaining the particle position corresponding to the minimum fitness function of each particle in an initial population and the particle position corresponding to the global minimum fitness function in the initial population; updating the velocity matrix, the position matrix, the particle position corresponding to the minimum fitness function of each particle, and the particle position corresponding to the global minimum fitness function; and obtaining the final analog low-pass filter according to the particle position corresponding to the global minimum fitness function. Thereby, the designed filter has a more flat passband and stronger stopband attenuation.

Description

technical field [0001] The invention belongs to the technical field of digital signal processing, in particular to a method for designing a finite-length impulse response digital low-pass filter. Background technique [0002] The finite impulse response digital filter (FIR) is the most basic component in the digital signal processing system. It can guarantee arbitrary amplitude-frequency characteristics while having strict linear phase-frequency characteristics. At the same time, its unit sampling response is finite. Thus the filter is a stable system. It has a wide range of applications in communications, radar, sonar, biomedicine, and seismic exploration. Traditional FIR digital filter design methods include frequency decimation method, window function method and uniform approximation method. The frequency extraction method and the window function method are simple and easy to implement, but it is not easy to accurately determine the boundary frequency of its passband an...

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Problems solved by technology

The main feature of the algorithm based on the local neighborhood search technology is that the amount of calculation is small, but it can only find the local optimal point, and the optimization performance is not ideal.

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