扩散式变阶数最大相关熵准则算法

固定阶数的分布式自适应滤波算法只有在待估计向量的阶数已知且恒定的情况下才能达到相应的估计精度,在阶数未知或时变的情况下算法的收敛性能会受到影响,变阶数的分布式自适应滤波算法是解决上述问题的有效途径.但是目前大多数分布式变阶数自适应滤波算法以最小均方误差(Mean square Error,MSE)准则作为滤波器阶数的代...

Diffusion Variable Tap-length Maximum Correntropy Criterion Algorithm

The fixed tap-length distributed adaptive filtering algorithm can achieve the corresponding estimation accuracy only when the tap-length of the unknown vector is assumed to be known as a prior and constant.The convergence performance of the algorithm deteriorates when the tap-length is unknown or time varying.Variable tap-length distributed adaptive filtering algorithm is an effective way to solve this problem.However,most of the distributed variable tap-length adaptive filtering algorithms use the minimum mean square error(MSE)criterion as the cost function of the tap-length,and the convergence of the algorithm is greatly affected under the impulsive noise environment.The maximum correntropy criterion is robust to impulse noise and has low computational complexity.In order to improve the estimation accuracy of the distributed variable tap-length adaptive filtering algorithm under the impulsive noise environment,the maximum correntropy criterion is used as the cost function,relevant results are substituted into the fixed tap-length diffusion maximum correntropy criterion algorithm,and thus a diffusion variable tap-length maximum correntropy criterion(DVTMCC)algorithm is proposed.By communicating with the nodes in the neighborhood,the proposed algorithm realizes the information fusion of the entire network by means of diffusion,which has advantages of a high estimation accuracy,a small calculation cost,etc.Simulation experiments compare the convergence performance of DVTMCC algorithm and other distributed variable tap-length adaptive filtering algorithms,and fix tap-length diffusion maximum correntropy criterion algorithm under the impulsive noise environment.Simulation results show that the DVTMCC algorithm can estimate the tap-length and weight vector of the unknown vector at the same time under the impulsive noise environment,and its performance is better than compared algorithms.