Understanding Steady State Coefficients in Adaptive Filters

AI Thread Summary
Determining when the coefficients of an adaptive filter reach steady state involves understanding whether they converge to a single value or remain bounded within a range. For FIR filters, the settling time to steady state is predetermined during the design phase. In contrast, IIR filters do not reach a final value but require numerical extrapolation to estimate their steady state due to their dependence on historical data. The discussion emphasizes the importance of filter architecture in understanding coefficient behavior. Overall, the nature of steady state in adaptive filters is crucial for effective implementation and performance evaluation.
nikki92
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If I am trying to find the steady state coefficients of a filter, when do I know the coefficients went into the steady state? In another words, steady state means it converged to a single value or that it is bounded between values? If say it is bounded between values how would I go about deciding what coefficients to use?
 
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Not sure of your filter architecture or what you are really doing. Sounds like an adaptive filter.
 
Is this to do with adaptive filters?

Are you wanting starting values for the filter coefficient of an adaptive filter?
Or are you wanting to know the values they take when adapted to a stable situation?

A FIR filter will settle to a steady state in a fixed time. You know how long it will take from the design.

The final steady state of an IIR filter must be extrapolated numerically as it will never quite get to a final value and history will always effect it to some extent.
 
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