Consensus conditions of continuous-time multi-agent systems with relative-state-dependent measurement noises and matrix-valued intensity functions

In this paper, we consider the distributed consensus of high-dimensional first-order agents with relative-state-dependent measurement noises. Each agent can measure or receive its neighbors' state information with random noises, whose intensity is a nonlinear matrix-valued function of agents' relative states. By the tools of stochastic differential equations and algebraic graph theory, we give sufficient conditions to ensure mean square and almost sure consensus and the convergence rate and the steady-state error for average consensus are quantified.