A Distributed Diffusion Kalman Filter in MultiTask Networks

The Distributed Diffusion Kalman Filter (DDKF) algorithm has earned great attention lately and shows an elaborate way to address the issue of distributed optimization over networks. Estimation tracking a single state vector collectively by nodes have been point focus. However, there are several multi-task-oriented issues where optimal for each node may not be same. This work considers sensor networks multi-task in which individual communicate with their immediate nodes. A diffusionbased is developed. done implementing unsupervised adaptive clustering process, aids forming clusters collaborating on tasks. gave rise effective level cooperation improving estimation accuracy, especially cases cluster's background experience unknown. To demonstrate efficiency our algorithm, computer simulations were conducted. Comparison carried out Filtermulti-task respect AdaptThen-Combine (ATC) diffusion schemes utilizing both static combination weights. Results showed that ATC performance combiners as compared combiners.