Comparison between ideal, linear and lowpass filter interpolators

In summary, the conversation discusses the use of low order low pass filters instead of Sinc filters for sampling and reconstruction. The ideal reconstruction using an ideal interpolator and the reconstruction using linear interpolation are compared, with a condition on the sampling rate to ensure a small difference between the two. This condition is generalized to specify the required sampling rate and pole position of a 1st order low pass filter. The use of frequency domain for comparison is suggested, with the Fourier transforms of a linear interpolator and a 1st order low pass filter discussed. The question also addresses the difference between the Fourier transforms of a linear interpolator and a Sinc interpolator.
  • #1
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Homework Statement

Q3. Sampling and reconstruction
A) It is common practice to use low order low pass filters instead of a Sinc filter, at the expense
of a higher sampling rate. Here we investigate how to do this.
Compare:
a) the ideal reconstruction of a low pass sampled signal, which can be achieved with an ideal
interpolator with V(t)=Σ(of n) (Vn sinc(πFs(t-tn ))
, where tn are the sampling instants and vn the voltages measured at these instants
with
b) the reconstruction that can be achieved with a linear interpolation
V(t) = Vn−1*(tn-t)/T+Vn*(t-tn-1)/T
between two samples taken at times tn−1 and tn .
Derive a condition on the sampling rate so that the difference between the two interpolations is smaller that a specified error δ .

Generalise this result to specify the required sampling rate and pole position of a 1st order
low pass filter used for the interpolation.

HINT: This is probably much easier to do in the frequency domain, by comparing the power
of the reconstructed signal to the original. After all, the reconstructed signal is the
convolution of the samples with the interpolating function.

What is the Fourier transform of a linear interpolator? How does it differ from the Fourier
transform of the Sinc interpolator (which is a square box in the frequency domain)?

What is the Fourier transform of a first order Low Pas filter, and how does it differ from that
of the ideal interpolator?

Homework Equations


The Attempt at a Solution


I am having trouble understanding the meaning of error δ, I tries to follow the Hint but I am not sure how comparing the power of an unknown signal would help. I calculated the Fourier transform of both interpolators but I am not sure how to continue from there. Just a clarification of what the question actually asks would be enough
 
Last edited:
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Welcome to PF. In order to receive assistance, you must attempt to solve the problem yourself and show your work. See the Rules tab
https://www.physicsforums.com/showthread.php?t=414380.
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Related to Comparison between ideal, linear and lowpass filter interpolators

1. What is the purpose of an interpolator?

An interpolator is used to estimate values between discrete data points. This is often necessary in signal processing and data analysis when the data is not uniformly sampled or when there are missing values.

2. What is the difference between an ideal, linear, and lowpass filter interpolator?

An ideal interpolator perfectly reconstructs the original signal without any loss of information. A linear interpolator uses a straight line to estimate values between data points. A lowpass filter interpolator uses a filter to smooth out the signal and reduce noise.

3. Which type of interpolator is the most accurate?

An ideal interpolator is the most accurate because it is able to perfectly reconstruct the original signal. However, this type of interpolator may not be feasible in real-world applications due to limitations in hardware and data. In these cases, a lowpass filter interpolator may be a better option as it can still provide a good estimation with reduced noise.

4. How does an interpolator affect the frequency response of a signal?

An interpolator can introduce distortions in the frequency response of a signal. This is especially true for lowpass filter interpolators as they filter out high frequency components in the signal. This can result in a loss of high frequency information and a change in the overall frequency response.

5. Can an interpolator be used to increase the sampling rate of a signal?

Yes, an interpolator can be used to increase the sampling rate of a signal. This is known as upsampling, where the interpolator is used to estimate values between the original data points, effectively increasing the number of samples in the signal. However, it's important to note that an interpolator cannot add new information to the signal, so the increased sampling rate may not necessarily improve the quality of the signal.

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