Abstract
In this chapter, we discuss the design of digital filters with finite-length impulse response using windowing and more important using iterative optimisation, i.e., mini-max design of linear-phase as well as nonlinear-phase filters, e.g., special cases as minimum-phase, half-band and Nyquist FIR filters, differentiators and Hilbert transformers. We discuss concepts like zero-phase response, delay- and allpass-complementary filter pairs, which have reduced realisation cost. Finally, we demonstrated the design of FIR filters with a least squares approximation using linear programming and quadratic programming. The chapter contains 27 solved examples.
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Notes
- 1.
After the Austrian, nineteenth century meteorologist, Julius van Hann. He is seen as the father of modern meteorology.
- 2.
Richard W. Hamming (1915–1998).
- 3.
Note that \({\text{wT}}\) should be normalized with respect to half the sampling frequency in the function firpmord. Hence, \({\text{wT}}\) should be divided by π, i.e., \([{\text{N, Be, D, W}}] = {\text{firpmord}}\,\,({\text{wT}}/{\text{pi, b, d}})\) Alternatively we may write [N, Be, D, W] = firpmord (wT, b, d, 2*pi) where b specifies the desired magnitude in the different bands, e.g., b = [1 0] and d specifies the maximum ripples allowable in each band, e.g., d = [δc δs] = [0.01 0.001].
- 4.
Note that in MATLAB the first element in the vector h is element 1. Hence, this element corresponds to the first value in the impulse response, i.e., h(1) ↔ h(0).
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Wanhammar, L., Saramäki, T. (2020). Synthesis of FIR Filters. In: Digital Filters Using MATLAB . Springer, Cham. https://doi.org/10.1007/978-3-030-24063-9_5
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