A low complexity wavelet packet transform-based least mean square (LMS) adaptive beamformer is presented in this paper. This beamformer uses wavelet packet transform as the preprocessing, reduces the signal dimension in wavelet packet domain for low complexity and denoising, and employs least mean square algorithm to implement adaptive beamformer. Theoretical analysis and simulations demonstrate that this algorithm with better beamforming performance converges faster than the conventional adaptive beamformer and the wavelet transform-based beamformer. Finally, our proposed algorithm has the low complexity, and it can be easy to implement.
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