稳定分布噪声下基于概率密度函数匹配的恒模波束形成

为抑制脉冲稳定分布噪声对波束形成的影响，采用信息论自适应学习理论，使得波束形成输出的概率密度函数和期望信号的概率密度函数匹配最大化，设计适用于稳定分布噪声下的恒模波束形成器，采用Parzen核方法得到数据的概率密度函数估计，利用随机梯度下降法对波束形成器的权重进行迭代更新，仿真实验表明在脉冲稳定分布噪声环境下，本文算法相比传统的恒模波束形成具有更高的输出信号干扰噪声比和更快的收敛速度。 

Constant Modulus Beamformer Based on Probability Density Function Matching for Impulsive Stable Noise

To reducing the degradation of the beamforming caused by the impulsive stable noise, a new constant modulus (CM) algorithm was proposed based on the maximum matching of the probability density functions between the desired signal and output signal inspired by the information adaptive learning. The Parzen kernel method was utilized for the probability density function estimation and the weight of the beamforming was updated by the stochastic steepest gradient. Computer simulations show that the proposed CM algorithm has higher output signal to interfere noise ratio and faster convergence than the conventional CM beamforming in stable distribution noise.