Distributed Nonlinear Estimation Over Unbalanced Directed Networks

Abstract

This paper investigates distributed nonlinear parameter estimation in unbalanced multi-agent networks, where individual agents sequentially make local, nonlinear, noisy measurements of the true but unknown parameter, and aim at estimating the parameter consistently by sharing their measurements with their neighbors via an arbitrarily predesigned directed communication topology. To this end, a distributed nonlinear least-squares estimator, termed as DNLS-AB, is proposed by using row- and column-stochastic matrices associated with the communication topology. Under the strong connectivity of the communication topology and several mild assumptions on the sensing model functions, it is shown that the DNLS-AB estimator leads to consistent parameter estimates if the stepsize is chosen small enough. Furthermore, the performance of the designed algorithm in terms of the asymptotic mean and asymptotic covariance of the error sequence between the weighted estimate sequence and the true value of the parameter is also analyzed and shown to be consistent with that of one widely used centralized estimator. Finally, a numerical example is given to validate the proposed algorithm.