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I need to interpolate a complex valued bandlimited periodic function using local interpolation.

I can have the signal sampled at any frequency I want over at equispace intervals.

I am aware that for bandlimited periodic functions the best interpolation method is global interpolation (FFT).

However, for this project I need to use local interpolation. I managed to perform the interpolation using Lagrange polynomials of order 10 but I require an oversampling of 2 to achieve the desired results. My goal is to reduce oversampling to any extend even if I need a longer interpolator.

I recently read as much as I could about FIR filters but somethings are not clear to me.

Since the interpolation factor is of the order of 2, I am planning to use a halfband pass interpolator (many books say this is the best since one gets a cheap interpolator due to the zeros in the impulse response).

My questions are:

  • How do I design a halfband pass filter? In Matlab there are nice functions in the dsp package that allow directly to construct such filters, unfortunately I have to do this implementation in C. I searched and I found that the remez() function from Octave to compute the coefficients is available in C and is the one I am planning to use.
    Designing a low pass filter with the Parks-Mclellan algorithm is it by default a halfband pass filter (provided I set up correctly the frequency bands) or do I have to enforce the time domain constraints?

  • Regarding the linear phase of the FIR filter, what I understand is that my signal will be shifted by some samples, how do I compensate to remove the shifting? What comes to my mind is to apply the corresponding shifting on the opposite direction, is this necessary?

  • If you could provide an working example of an interpolation of a periodic function (e.g. sum of sines) using a FIR I would be very grateful.
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  • $\begingroup$ Reading more about this topic, I found out that for my project I require zero-phase filters since this is an "offline" application. $\endgroup$
    – user1084135
    May 23, 2018 at 19:00
  • $\begingroup$ It's an advantage of offline signal processing that non-causal filters can be used, and hence, zero-phase filtering is possible. It's not a necessity. $\endgroup$
    – Marcus Müller
    Apr 27, 2019 at 17:08
  • $\begingroup$ and no, "global interpolation (FFT)" (what's that?) is not inherently the "best" option; I think you might be thinking of padding the FFT of the original signal with zeros and then transforming it back. That has very serious problems! $\endgroup$
    – Marcus Müller
    Apr 27, 2019 at 17:11
  • $\begingroup$ For remez half-band filters that has almost half of the coefficients zero-valued, see my answer to FIR Filter design: Window vs Parks-McClellan and Least-Squares. $\endgroup$
    – Olli Niemitalo
    Apr 27, 2019 at 17:57
  • $\begingroup$ @MarcusMüller, when the signal is periodic, bandlimited, and you have a complete period, what kind of interpolation is better than global interpolation using trigonometric polynomials and implemented with the FFT? What are the problems you refer to? $\endgroup$
    – user1084135
    Apr 29, 2019 at 6:29

1 Answer 1

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The impulse response and frequency response of a length N zero-phase FIR lowpass filter can be designed using the Remez exchange algorithm.

N = 11; % filter length - must be odd

b = [0 0.1 0.2 0.5]*2; % band edges

M = [1 1 0 0 ]; % desired band values

h = remez(N-1,b,M); % Remez multiple exchange design

The impulse response h is returned in linear-phase form, so it must be left-shifted (N-1)/2 = 5 samples to make it zero-phase. Answer taken from https://www.researchgate.net/post/How_can_I_design_a_null_phase_FIR_filter.

I implemented this solution some time ago and still works very well in my code.

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  • $\begingroup$ I do not understand why this answer is marked as not useful. It solved the question and works very well. $\endgroup$
    – user1084135
    Apr 29, 2019 at 6:07

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