Abstract
We detail in this paper an -norm normalized subband adaptive filter(-NSAF) algorithm applied to improve the convergence speed and steady-state error whose solutions have some degree of sparsity, for example, the wideband beamforming in uniform linear arrays (ULA). Wideband beamformer consists of sensors and a series of tapped delay-lines, in order to update the frequency dependent weights. However, as the number of sensors increases, there are large number of weights need to be updated. Some conventional algorithms such as NLMS and NSAF algorithm can not exploit the latent sparsity of wideband beamformer, so the convergence speed of these algorithms is relative slow. Therefore, -NSAF which takes advantage of the sparsity is proposed to be used in the wideband beamformer, aiming to improve the convergence speed and low steady-state error. Finally, computer simulation results indicate that the proposed algorithm achieves an improved wideband beamforming performance compared with other adaptive methods.
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