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

Distributed Least Squares Algorithm for Continuous-time Stochastic Systems Under Cooperative Excitation Condition