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In this article, we consider the problem of discrete-time, diffusion-based distributed parameter estimation with the agents connected via directed graphs with switching topologies and a self loop at each node. We show that, by incorporating the recently introduced dynamic regressor extension and mixing procedure to a classical gradient-descent algorithm, improved convergence properties can be achieved. In particular, it is shown that with this modification sufficient conditions for global convergence of all the estimators is that one of the sensors receives enough information to generate a consistent estimate and that this sensor is “well-connected.” The main feature of this result is that the excitation condition imposed on this distinguished sensor is strictly weaker than the classical persistent excitation requirement. The connectivity assumption is also very mild, requiring only that the union of the edges of all connectivity graphs over any time interval with an arbitrary but fixed length contains a spanning tree rooted at the information-rich node. In the case of nonswitching topologies, this assumption is satisfied by strongly connected graphs, and not only by them. [ABSTRACT FROM AUTHOR]

We present some new results on the dynamic regressor extension and mixing parameter estimators for linear regression models recently proposed in the literature. This technique has proven instrumental in the solution of several open problems in system identification and adaptive control. The new results include the following, first, a unified treatment of the continuous and the discrete-time cases; second, the proposal of two new extended regressor matrices, one which guarantees a quantifiable transient performance improvement, and the other exponential convergence under conditions that are strictly weaker than regressor persistence of excitation; and, third, an alternative estimator ensuring convergence in finite-time whose adaptation gain, in contrast with the existing one, does not converge to zero. Simulations that illustrate our results are also presented. [ABSTRACT FROM AUTHOR]

A new procedure to design parameter estimators with enhanced performance is proposed in the technical note. For classical linear regression forms, it yields a new parameter estimator whose convergence is established without the usual requirement of regressor persistency of excitation. The technique is also applied to nonlinear regressions with “partially” monotonic parameter dependence—giving rise again to estimators with enhanced performance. Simulation results illustrate the advantages of the proposed procedure in both scenarios. [ABSTRACT FROM PUBLISHER]

The problem of parametric estimation of harmonic signals in the presence of noise and computational constraints is an important issue in many engineering fields. This technical note proposes an algorithm for frequency and bias estimation under noisy measurements. An approach for high-frequency noise rejection by switching is proposed and applied for improvement of the estimation accuracy. Efficiency of the approach is demonstrated through numerical simulations. [ABSTRACT FROM AUTHOR]