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为了解决传统最小均方(LMS)自适应波束形成算法在低信噪比环境下收敛速度较慢的问题,提出了一种快速收敛的小波域自适应波束形成算法.该算法利用小波变换软阈值法消除信号中的加性高斯白噪声,并在此基础上将牛顿法应用于LMS算法中,提高了小波域LMS算法的收敛速度.仿真结果表明,相比传统LMS算法,在低信噪比环境下,该算法收敛速度加快,稳态误差减小,波束形成精确度有较大的提高;同时相对于已有的小波域LMS算法,该算法的收敛速度和精度也有所提高.
In order to solve the problem that the traditional Least Mean Square (LMS) adaptive beamforming algorithm converges slowly in low signal-to-noise ratio environment, a fast convergent wavelet domain adaptive beamforming algorithm is proposed. This algorithm uses wavelet transform soft threshold Method to eliminate additive Gaussian white noise in the signal, and based on this, Newton method is applied to LMS algorithm to improve the convergence speed of LMS algorithm in wavelet domain.The simulation results show that compared with the traditional LMS algorithm, The convergence speed of the algorithm is quicker, the steady-state error is reduced and the accuracy of beamforming is greatly improved. Meanwhile, compared with the existing LMS algorithm in wavelet domain, the convergence speed and precision of the algorithm are also improved.