## LMS data self correlation problem

Started by 5 years ago●4 replies●latest reply 5 years ago●107 viewsI have implemented an adaptive filter using an LMS based approach. The purpose of the filter is to subtract a signal X from a signal Y where Y is assumed to have been X convolved with a filter H,

i.e. Error = Y - X H_hat. Normal NLMS filter update is used.

My implementation works great until X becomes a highly periodic signal(periodicity is within the length of H). At this point the filter update goes a bit bananas. If anyone knows a good way of controlling such problems I'd be very grateful, thanks.

Apologies for the poor description, I've now looked further into what is happening. My observations are that if I use uncorrelated broadband noise as my input for X then the filter converges very well. If however the X signal is switched for a pure sine of 1kHz then the filter H goes unstable.

My observation is that the update to H is now producing many "false" correlations due to the periodicity of X.

Thank you for your suggestion of adding leakage, I am trying to adaptively add that when I detect that this condition is met. The condition is easy to spot as the gain of the filter gets very high.

Thank you