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Precise and rapid extraction of spherical target features from laser point clouds is critical for achieving high-precision registration of multiple point clouds. Existing methods often use linear models to represent spherical target characteristics, which have several drawbacks. This paper proposes a rigorous estimation algorithm for spherical target features based on least squares configurations, in which the point-cloud data error is used as a random parameter, while the spherical center coordinates and radius are used as nonrandom parameters, emphasizing correlation between spherical parameters. The implementation details of this algorithm are illustrated by deriving calculation formulas for three variance–covariance matrices: variance–covariance matrices of the new observations, variance–covariance matrices of the new observation noise, and variance–covariance matrices of random parameters and the new observation noise. Experiments show that the estimation accuracy of sphere centers using our method is improved by at least 5.7% compared to classical algorithms, such as least squares, total least squares, and robust weighted total least squares.

We investigate the performance of distributed estimation of static parameters in networks whose nodes make correlated measurements. The measurement models are linear with node specific time-varying observation matrices. The nodes cooperate with their neighbors by exchanging information that allow for approximation of the sufficient statistics in the estimation. We prove that the proposed estimation method is efficient. Specifically, we show that the performances of the distributed estimator and the centralized one are asymptotically the same, i.e., the limit of the ratio of their variances is 1. [ABSTRACT FROM AUTHOR]

The constrained least absolute deviation (L1) estimator is an attractive alternative to both the unconstrained L1 estimator and the least-square estimator. This paper introduces a constrained L1 method and proposes two cooperative recurrent neural networks (CRNNs) for the constrained L1 estimator. Unlike existing cooperative neural networks, the proposed two CRNNs have a novel weighting cooperation scheme to integrate individual neural network information automatically. As a special case, the proposed continuous-time CRNN includes the existing continuous-time neural network for unconstrained L1 estimator. Compared with existing continuous-time neural networks for the constrained L1 estimator, the proposed continuous-time CRNN has a lower model complexity and the finite-time convergence to the exact optimal solution without any additional condition. Furthermore, compared with existing numerical algorithms for the constrained L1 estimator, in addition to a low computational complexity, the proposed two CRNNs are suitable for parallel implementation and can deal with the L1 estimation problem with degeneracy. The proposed two CRNNs are applied to parameter estimation problems under non-Gaussian noise environments. Simulation results demonstrate that the proposed CRNNs are indeed effective in dealing with the L1 estimation problem with nonunique solutions and in obtaining a better solution than relevant algorithms. [ABSTRACT FROM AUTHOR]

The use of linear parameter estimation techniques to determine the rotor resistance, self-inductance of the rotor winding, as well as the stator leakage inductance of a three-phase induction machine is investigated in this paper. In order to obtain results with maximum accuracy, some specific procedures to reduce the effect of the operating conditions on the quality of the estimates are investigated. For analytical identification, a model is developed from the steady-state equations of induction motor dynamics. The identification procedure, based on a simple algorithm derived from least squares techniques, uses only the information of stator currents and voltages and rotor angular speed as input–output data. The computer simulation as well as the experimental results are used to anchor the main conclusions issued from this study and to demonstrate the practical use of the identification method. [Copyright &y& Elsevier]