论文标题
自适应网络上的强大多任务扩散归一化M-估计子带自适应滤波器
Robust Multitask Diffusion Normalized M-estimate Subband Adaptive Filtering Algorithm Over Adaptive Networks
论文作者
论文摘要
近年来，多任务扩散最小均方（MD-LMS）算法已广泛应用于多任务网络的分布式参数估计和目标跟踪。但是，其性能主要受两个方面的限制，即相关的输入信号和冲动噪声干扰。为了同时克服这两个局限性，本文首先将子带自适应过滤器（SAF）引入多任务网络。 Then, a robust multitask diffusion normalized M-estimate subband adaptive filtering (MD-NMSAF) algorithm is proposed by solving the modified Huber function based global network optimization problem in a distributed manner, which endows the multitask network strong decorrelation ability for correlated inputs and robustness to impulsive noise interference, and accelerates the convergence of the algorithm significantly.与可靠的多任务扩散仿射投影M-估计（MD-APM）算法相比，所提出的MD-NMSAF的计算复杂性大大降低了。此外，还提供了MD-NMSAF的理论瞬态和稳态网络均方根偏差（MSD）的稳定性条件，并通过计算机模拟提供并验证。在不同的输入信号和冲动噪声环境下，在稳态准确性和跟踪速度方面，MD-NMSAF算法与其他竞争对手相比，MD-NMSAF算法的性能优势完全证明了MD-NMSAF算法的性能优势。
In recent years, the multitask diffusion least mean square (MD-LMS) algorithm has been extensively applied in the distributed parameter estimation and target tracking of multitask network. However, its performance is mainly limited by two aspects, i.e, the correlated input signal and impulsive noise interference. To overcome these two limitations simultaneously, this paper firstly introduces the subband adaptive filter (SAF) into the multitask network. Then, a robust multitask diffusion normalized M-estimate subband adaptive filtering (MD-NMSAF) algorithm is proposed by solving the modified Huber function based global network optimization problem in a distributed manner, which endows the multitask network strong decorrelation ability for correlated inputs and robustness to impulsive noise interference, and accelerates the convergence of the algorithm significantly. Compared with the robust multitask diffusion affine projection M-estimate (MD-APM) algorithm, the computational complexity of the proposed MD-NMSAF is greatly reduced. In addition, the stability condition, the analytical expressions of the theoretical transient and steady-state network mean square deviation (MSD) of the MD-NMSAF are also provided and verified through computer simulations. Simulation results under different input signals and impulsive noise environment fully demonstrate the performance advantages of the MD-NMSAF algorithm over some other competitors in terms of steady-state accuracy and tracking speed.
