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fir-design-window-method-'s Introduction

FIR Design (Window Method)

In this section, we implement a Python FIR (Finite Impulse Response) filter design class using the window method. We use a design table based on the DSP textbook to construct this class.

Window Functions

We consider the following window functions:

  • Rectangular
  • Hanning
  • Hamming
  • Blackman
  • Kaiser

Filter Design Parameters

Window Transition Passband Main/Side Lobe Stopband
Function Width (Hz) Ripple Relation (dB) Attenuation (dB)
Rectangular 0.9 / N 0.7416 13 21
Hanning 3.1 / N 0.0546 31 44
Hamming 3.3 / N 0.0194 41 53
Blackman 5.5 / N 0.0017 57 74
Kaiser 2.93 / N 0.0274 β = 4.54 50
4.32 / N 0.00275 β = 6.76 70
5.71 / N 0.000275 β = 8.96 90

Filter Types

We consider the following filter types:

  • Lowpass (LPF)
  • Highpass (HPF)
  • Bandpass (BPF)
  • Bandreject (BRJF)

Ideal Impulse Responses

Type of Filter Ideal Impulse Response
hd[n], n ≠ 0 hd[0]
Lowpass Formula 1 2fc
Highpass Formula 2 1-2fc
Bandpass Formula 3 2(f2-f1)
Bandreject Formula 4 1-2(f2-f1)

Formulas

Formula 1:

$$ 2fc \cdot \frac{\sin(nwc)}{nwc} $$

Formula 2:

$$ -2fc \cdot \frac{\sin(nwc)}{nwc} $$

Formula 3:

$$ 2f_{2} \cdot \frac{\sin(nwc_{2})}{nwc2}-2f_{1} \cdot \frac{\sin(nwc_{1})}{nwc_{1}} $$

Formula 4:

$$ 2f_{1} \cdot \frac{\sin(nwc_{1})}{nwc_{1}}-2f2 \cdot \frac{\sin(nwc2)}{nwc2} $$

Application

$$ y[n] = \sum_{k=0}^{N} h[k] \cdot x[n-k] $$



Authors: D.S. 、 Yuyu378

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