Diffusion Constrained Adaptive Filtering Algorithm Based on Half-Quadratic Criterion for System Identification
针对经典扩散自适应滤波算法无法处理约束系统辨识的问题，提出一种基于半二次准则的扩散约束算法，增强节点对脉冲噪声的鲁棒性，并通过相邻节点数据共享提升网络收敛性能。
Classical diffusion adaptive filtering algorithms are often designed to deal with unconstrained system identification. However, they are not suited to constrained system identification. This work develops a diffusion constrained adaptive filtering algorithm to address the above problem. We use the cost function based on the half-quadratic criterion (HQC) to enhance the robustness of the nodes against impulsive noise. We also utilize the strategy of data sharing between the neighboring nodes to promote the network convergence performance. To investigate the stochastic behavior, we further analyse the stability condition and the transient and steady-state performance of the proposed diffusion constrained HQC (DCHQC) algorithm under several commonly used statistical assumptions. Simulation results are given to show the advantages of DCHQC in the context of distributed constrained system identification and beamforming and to validate the theoretical expressions on the performance prediction.