A minimised complexity dynamic structure adaptive filter design for improved steady state performance analysis

摘要

The structural complexity and overall performance of the adaptive filter depend on its structure. The number of taps is one of the most important structural parameters of the liner adaptive filter. In practice the system length is not known a priori and has to be estimated from the knowledge of the input and output signals. In a system identification framework the tap-length estimation algorithm automatically adapts the filter order to the suitable optimum value which makes the variable order adaptive filter a best identifier of the unknown plant. In this paper an improved pseudo-fractional tap-length selection algorithm is proposed to find out the optimum tap-length which best balances the complexity and steady state performance. The performance analysis is presented to formulate steady state tap-length in correspondence with the proposed algorithm. Simulations and results are provided to observe the analysis and to make a comparison with the existing tap-length learning methods.