6 accepted papers
This paper presents a model-free solution to distributed transmit beamforming using mobile agents. Each agent is equipped with an antenna and the agents represent the individual elements in an antenna array. The agents are tasked to coordinate their relative location, phase offsets, and amplitude to…
Motivated by decentralized approaches to machine learning, we propose a collaborative Bayesian learning algorithm taking the form of decentralized Langevin dynamics in a non-convex setting. Our analysis show that the initial KL-divergence between the Markov Chain and the target posterior distributio…
This paper is concerned with the problem of bearings based multi-agent circumnavigation of a maneuvering target. Agents are assumed to have access to their own individual bearing measurements as well as the ones from their immediate neighbors. The aim is to devise a distributed algorithm to estimate…
This paper presents a novel distributed algorithm for tracking a highly maneuvering target using bearing or direction of arrival measurements. The proposed approach is built on the dynamic average-consensus algorithm, which allows a networked group of agents (nodes) to reach consensus on the global…
In wireless sensor networks, estimating a global parameter from locally obtained measurements via local interactions is known as the distributed parameter estimation problem. Solving these problems often require the deployment of distributed optimization algorithms that rely on a constant exchange o…
This paper presents the formulation and analysis of a novel distributed maximum likelihood algorithm that utilizes a first-order optimization scheme. The proposed approach utilizes a static average consensus algorithm to reach agreement on the initial condition to the iterative optimization scheme a…