This work studies how to estimate the mean-field density of large-scale systems in a distributed manner. Such problems are motivated by recent swarm control technique that uses approximations represent collective effect swarm, wherein (especially its gradient) is usually used feedback design. In first part, we formulate estimation problem as filtering associated partial differential equation (P...

Communication systems are affected by channel distortions. Impulsive noise is one of the significant factors for impairments. The standard additive white Gaussian (AWGN) model and conventional estimation algorithms like least mean square (LMS) its variants tend to be ineffective under such conditions. This paper presents a robust adaptive algorithm using Geman-McClure estimator in diffusion-bas...

Consider discrete-time linear distributed averaging dynamics, whereby agents in a network start with uncorrelated and unbiased noisy measurements of common underlying parameter (state the world) iteratively update their estimates following non-Bayesian rule. Specifically, let every agent her estimate to convex combination own current those neighbors network. As result this iterative averaging, ...

In this paper, we consider distributed simultaneous state and parameter estimation for a class of nonlinear systems, which the augmented model comprising both states parameters is only partially observable. Specifically, first illustrate how sensitivity analysis (SA) can select variables estimation. Then, community structure detection (CSD) based process decomposition method proposed dividing e...