Towards optimally structured lattice RLS filters using a variable tap-length scheme

Lattice recursive least-squares (LRLS) adaptive filters are well known for their fast convergence to achieve the minimum least-squares error. This accelerated decaying error property, however, comes with a high cost filter structure. Using a variable tap-length technique known as the fractional tap-length (FT) algorithm, this article provides an adaptive method to search for the optimum length of LRLS filters. To validate the performance of the proposed algorithm, simulations are conducted under low noise and high noise conditions. All the simulation results show the advantages of the proposed algorithm compared with fixed length LRLS filters as well as with the fractional tap-length lattice based least-mean-squares (FT-LLMS) algorithm. With a fast convergence rate and a small steady-state error of the tap-length, the proposed algorithm can efficiently track any change in the impulse response and estimate the filter's optimum structure.