Cooperative constrained parameter estimation by ADMM-RLS

With recent advances in cloud computing, resources with customizable computational power and memory can be exploited to store and analyze data collected from large sets of devices. Although one can exploit the connection to the cloud to perform all the desired tasks on the cloud itself, in many applications it is also desirable to retrieve and process information locally. In this paper, we present a collection of cloud-aided consensus-based Recursive Least-Squares (RLS) estimators. The approaches are tailored to handle linear and nonlinear consensus constraints and limitations on parameter ranges. All the methods are designed so that raw measurements collected at the device level are processed by the device itself, requiring minimal changes to (possibly pre-existing) RLS estimators. The local estimates are then recursively refined and fused on the cloud to reach consensus among the devices.