基于P范数的变尺度扩散公平代价算法

Variable Scale Factor Diffusion Fair Algorithm Based on P-norm

为了进一步提高扩散式自适应滤波算法在不同噪声环境下的性能,提出了一种新的基于P范数的变尺度扩散公平代价函数算法。该算法在公平代价函数的基础上,对误差绝对值项加入了P范数,同时利用类箕舌线函数构造了一个随误差变化的尺度因子来共同控制算法的陡峭程度,进而使算法拥有更快的收敛速度和更小的稳态误差。在高斯噪声环境以及Alpha稳定分布和伯努利-高斯分布的非高斯噪声环境中的仿真结果表明,所提算法拥有更强的鲁棒性和更低的稳态误差,在不同噪声环境下的性能均优于对比算法。

A new P-norm based variable scale DFAIR diffusion fair cost function algorithm is proposed to further enhance the performance of diffusion based adaptive filtering algorithms in different noise environments.The absolute value of the error term is augmented with a P-norm and a scale factor that varies with the error is established using a tongue-like function to control the steepness of the algorithm.As a result,the convergence speed of the algorithm is accelerated and the steady-state error is reduced.The simulation results in Gaussian noise environments,as well as non-Gaussian noise environments with alpha stable distribution and Bernoulli Gaussian distribution,demonstrate the algorithm has stronger robustness and lower steady-state error.The performance of the proposed algorithm surpasses that of the comparison algorithm in various noise environments.