Distributed Least Squares Algorithm of Continuous-Time Stochastic Regression Model Based on Sampled Data

In this paper, the authors consider the distributed adaptive identification problem over sensor networks using sampled data, where the dynamics of each sensor is described by a stochastic differential equation. By minimizing a local objective function at sampling time instants, the authors propose an online distributed least squares algorithm based on sampled data. A cooperative non-persistent excitation condition is introduced, under which the convergence results of the proposed algorithm are established by properly choosing the sampling time interval. The upper bound on the accumulative regret of the adaptive predictor can also be provided. Finally, the authors demonstrate the cooperative effect of multiple sensors in the estimation of unknown parameters by computer simulations.