Trimmed diffusion least mean squares for distributed estimation

We consider the problem of distributed estimation, where a set of nodes is required to collectively estimate network parameters from noisy measurements. The problem is important when modeling a wide class of real-time sensor networks, where efficiency, robustness, and low power consumption are desired features. In this work, we focus on diffusion-based adaptive solutions that capable to avoid undue influence from outliers, especially in the presence of impulsive noise or dysfunction of certain nodes. We motivate and propose trimmed diffusion least mean square (TDLMS) algorithm that selects normal neighborhood to update the system estimation. We provide performance analysis together with simulation results comparing with existing methods.