Optimal Design Of Subband Adaptive Filter And Its FPGA Implementation

Adaptive filters are widely used in system identification,channel equalization,echo cancellation,active noise control,and so on.When the correlation of the input signal is relatively high,classical least-mean-square adaptive filters have a slow convergence rate.Due to the decorrelating of the normalized subband adaptive filter,it can converge fast.When the measurement noise contains impulsive interference,although the sign subband adaptive filter can restrain the effect of the impulsive interference,it will suffer from large estimation bias with noisy filter inputs.To overcome this problem,this thesis proposes a bias-compensated sign subband adaptive filter.It can reduce the estimation bias by adding a bias-compensated term.Besides,it also uses a time-varying regularization parameter to produce smaller steady-state misalignment.When the unknow system is sparse,the zero-attraction method can be applied to promote the performance of adaptive filtering This thesis develops a variable-parameter sparse subband adaptive filter based on the minimization of mean-square deviation.This adaptive filter can not only make full use of the sparsity of the unknown system to accelerate its convergence rate but also address the tradeoff problem between convergence rate and steady-state misalignment.In view of the importance of hardware implementation of subband adaptive filter,this thesis implements subband adaptive filter with FPGA by using distribute arithmetic and divided-interval division.The implementation method effectively reduces the consumption of resources and improves the clock frequency of the system.