  In this paper, we study the distributed adaptive estimation problem of
continuous-time stochastic dynamic systems over sensor networks where each
agent can only communicate with its local neighbors. A distributed least
squares (LS) algorithm based on diffusion strategy is proposed such that the
sensors can cooperatively estimate the unknown time-invariant parameter vector
from continuous-time noisy signals. By using the martingal estimation theory
and Ito formula, we provide upper bounds for the estimation error of the
proposed distributed LS algorithm, and further obtain the convergence results
under a cooperative excitation condition. Compared with the existing results,
our results are established without using the boundedness or persistent
excitation (PE) conditions of regression signals. We provide simulation
examples to show that multiple sensors can cooperatively accomplish the
estimation task even if any individual can not.
