小波域的自适应波束形成算法

 在分析传统自适应波束形成的基础上,首次提出了一种小波域的自适应波束形成算法。与通常的自适应波束形成算法相比,该算法利用小波变换对小波空间进行了分解,信号经小波变换自相关性会下降,收敛速度提高,同时在此分解过程中,根据信号与白噪声在不同尺度上的小波变换模极大值表现完全不同的特性进行信号的消噪。理论分析和仿真结果表明了该算法收敛速度较快,且计算量增加较少,易于实时实现,而且具有较好性能。同时仿真实验表明该算法收敛速度与小波基和尺度的选择有关,尺度越大收敛速度越快;对于同一小波基系列,小波基的正则性越好收敛速度越快。

Wavelet Domain Adaptive Beamforming Algorithm

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.