Abstract: Wavelet transform is employed to adaptive beamforming for the first time and wavelet domain adaptive beamforming algorithm is presented in this paper. The received signal of array antennas is analyzed, and the analysis shows that the received signal has multi-resolution characteristics. So the wavelet can be used to array signal processing. This novel adaptive beamforming algorithm uses wavelet transform as the preprocessing, and the wavelet transformed signal uses LMS algorithm to implement adaptive beamforming in wavelet domain. This algorithm makes use of wavelet transform to divide the wavelet space, which shows that wavelet transform has the better decorrelation ability and leads to better convergence. White noise can be wiped off under wavelet transform according to different characteristics of signal and white noise under the wavelet transform. Theoretical analysis and simulation results demonstrate that this algorithm converges faster than the conventional adaptive beamforming algorithm, and it has the better performance and smaller calculation. Simulation results also reveal that the algorithm convergence performance relates to wavelet base and scale, and show that the algorithm convergence gets better with scale increasing, and that for the same series of wavelet base the algorithm convergence gets better with wavelet base regularity increasing. Finally the algorithm is no more complex and can be implemented easily, so it can be used widely.

Key words: adaptive beamforming, wavelet transform, multi-resolution analysis, array signal processing