Upsampled input to an Adaptive filter?

In summary, the speaker is discussing their experience using a standard LMS filter in MATLAB for a system identification task. They initially tried using a vector of Gaussian numbers as input and desired response, but the estimated weight vector did not match their expectations. They then tried upsampling and low pass filtering the input vector, but still obtained unexpected results. The speaker is asking for help in identifying their mistake and is open to suggestions and discussion.
  • #1
khurram usman
87
0
I will try to explain the issue I am having as clearly as possible without going into my coding or maths. I have my own and a MATLAB Central implementation pf standard LMS in MATLAB. Fixed step size. No normalization or other stuff.

I am trying to use it in a system identification setup. I generate a vector of gaussian numbers using "randn" and give the same vector as input and desired response to the LMS filter. Now the estimated weight vector at the end should be a "delta" channel and this is what I get. Then i tried upsampling and interpolating the input vector by an integer number and repeating the same thing. This time around the estimated channel is of the shape of an "Sinc". I gave the interpolated signal as the input and the desired response as before. No changes.

Then i also tried low pass filtering the input vector and repeating the same thing. Again a "Sinc". Has anyone observed this before or know something about this? Please point out my mistake. Any suggestions or a discussion is also welcome.
 
Engineering news on Phys.org

1. What is an adaptive filter?

An adaptive filter is a mathematical algorithm that adjusts its parameters based on the input data in order to minimize a certain error or achieve a desired output. It is commonly used in signal processing applications to remove noise or interference from a signal.

2. What is upsampling in the context of adaptive filters?

Upsampling is the process of increasing the sampling rate of a signal by inserting zeros in between the original samples. It can be used in conjunction with an adaptive filter to improve its performance, especially in cases where there is a high level of noise or interference in the input signal.

3. What are the benefits of using upsampling in an adaptive filter?

Upsampling allows for a more detailed representation of the input signal, which can help the adaptive filter to better distinguish between the desired signal and noise. This can lead to improved filter convergence and better noise reduction.

4. How does upsampling affect the computational complexity of an adaptive filter?

Upsampling increases the number of input samples, which in turn requires more computations to be performed by the adaptive filter. This can lead to a higher computational complexity, but it is often worth it for the improved performance achieved with upsampling.

5. Are there any drawbacks to using upsampling in an adaptive filter?

The main drawback of upsampling is the increased computational complexity, which can make real-time implementation challenging. Additionally, upsampling may not always improve the performance of an adaptive filter, as it depends on the characteristics of the input signal and the specific application.

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