Upsampled input to an Adaptive filter?

AI Thread Summary
The discussion revolves around the challenges faced when using a standard LMS filter in a system identification setup with Gaussian input signals. The user reports that while the estimated weight vector correctly represents a "delta" channel with the original input, upsampling and low-pass filtering lead to an estimated channel shaped like an "Sinc." The user seeks insights into why this occurs and requests suggestions for troubleshooting the issue. Additionally, there is a prompt for more details regarding the signal's bandwidth and the cutoff settings of the low-pass filter. The conversation highlights the complexities of adaptive filtering in relation to signal processing techniques.
khurram usman
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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.
 
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