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Distribution systems require ad hoc estimators, distribution system state estimation (DSSE) techniques, to acquire knowledge about the system status. An incorrect evaluation of the accuracy of the DSSE creates decision risks in network management. The possible variations in the network parameter values and the decays of the metrological characteristics of the measurement system elements are uncertainty sources very often not considered. Considering these possible lacks of accuracy, this paper focuses on the robustness of distributed measurement systems aimed to obtain accurate DSSE results. The problem of the proper assessment of the accuracy of the DSSE results obtained through a weighted least squares (WLS) approach is faced. A method capable of including different uncertainty sources in the uncertainty estimation of the WLS approach is presented. Furthermore, this paper proposes an optimal meter placement algorithm robust with respect to possible malfunctions in measurement system components. The results obtained on a portion of an Italian distribution network, along with their accuracy, are presented and discussed. [ABSTRACT FROM AUTHOR]

Linear estimators have been extensively utilized for wireless location estimation for their simplicity and closed form property. In the paper, the class of linear estimator by introducing an additional variable, e.g., the well-adopted linear least squares (LLS) estimator, is discussed. There exists information loss from the linearization of location estimator to the nonlinear location estimation, which prevents the linear estimator from approaching the Cramér-Rao lower bound (CRLB). The linearized location estimation problem-based CRLB (L-CRLB) is derived in this paper to provide a portrayal that can fully characterize the behavior for this type of linearized location estimator. The relationships between the proposed L-CRLB and the conventional CRLB are obtained and theoretically proven in this paper. As suggested by the L-CRLB, higher estimation accuracy can be achieved if the mobile station (MS) is located inside the convex hull of the base stations (BSs) compared to the case that the MS is situated outside of the geometric layout. This result motivates the proposal of geometry-assisted localization (GAL) algorithm in order to consider the geometric effect associated with the linearization loss. Based on the initial estimation, the GAL algorithm fictitiously moves the BSs based on the L-CRLB criteria. Two different implementations, including the GAL with two-step least squares estimator (GAL-TSLS) and the GAL with Kalman filter (GAL-KF), are proposed to consider the situations with and without the adoption of MS's historical estimation. Simulation results show that the GAL-KF scheme can compensate the linearization loss and improve the performance of conventional location estimators. [ABSTRACT FROM AUTHOR]

In this paper, we analyze and compare the properties of different well-known and also new nonparametric discrete Fourier transform (DFT)-based methods for resonant frequency and logarithmic decrement estimation in application to mechanical spectroscopy. We derive a new DFT interpolation algorithm for a signal analyzed with Rife–Vincent class-I windows and also propose new formulas that extend Bertocco and Yoshida methods. We study errors of the resonant frequency and logarithmic decrement estimation in realistic conditions that include measurement noise and a zero-point drift. We also investigate the systematic errors of the estimation methods of interest. A nonlinear least squares time-domain parametric signal fitting is used to determine the boundaries of statistical efficiency in all tests. [ABSTRACT FROM AUTHOR]

This paper proposes a light, flexible, and yet reliable multiparametric estimation algorithm for grid-connected voltage-source-converter-based systems. This approach combines the nonlinear least squares (NLSQ) fitting algorithm and the steady-state extended harmonic domain (EHD) model to take into account the harmonic effects. The EHD model is used to take advantage of the harmonic content of the electrical signals to provide robustness and improved performance to the NLSQ estimation methodology. In addition, this harmonic formulation reduces the necessity of synchronized measurements. Details of the implementation are provided by an experimental case study, in which the grid equivalent (Thevenin voltage, inductance, and resistance), the ac-side filter (inductance and resistance), and the converter switching and conduction loss resistance are accurately estimated for three different operating conditions. [ABSTRACT FROM AUTHOR]

In some complex environments, nonline-of-sight (or multipath) usually introduces an additional path delay to the pseudorange, which is known to be one of the most dominant sources of accuracy degradation in global navigation satellite systems (GNSSs). The additional path error is non-Gaussian. Different techniques, including antenna-based, receiver-based, and poster-receiver, have been proposed for mitigating the influence of multipath. However, the estimated additional path delay obtained by classical algorithms, such as maximum likelihood or Bayesian estimation, often has a certain deviation from the real-additional path delay. For more accurate estimation, we first restrict the additional path error in a relatively wide interval; then, the position estimation is converted into a constrained separable nonlinear least squares problem. We propose, in this paper, a two-stage estimation algorithm for global positioning system positioning. At the first stage, we ignore the additional path error and obtain a relatively accurate initial position. Then, based on the result obtained at the first stage, we include the additional path error in the estimation problem and estimate it by variable projection (VP) method. Numerical results show that the proposed algorithm based on the VP method can effectively mitigate multipath interference. [ABSTRACT FROM AUTHOR]

This paper presents a weighted least squares-based intrafield de-interlacing algorithm. First, we formulate the estimation of the missing pixels as a maximum a posteriori (MAP) framework. We deduce the weighted least squares structure from MAP based on the analysis of the statistic model of the original high-resolution images and the associated statistical model of the given low-resolution images and original high-resolution images. The weights affect the estimation of the statistical model. We also design adaptive weights to match regions with different properties. The method is compared with other de-interlacing algorithms in terms of PSNR and SSIM objective quality measures and de-interlacing speed. It was found to provide excellent performance and the best quality–speed tradeoff among the methods studied. [ABSTRACT FROM AUTHOR]

Soft-decision adaptive interpolation (SAI) provides a powerful framework for image interpolation. The robustness of SAI can be further improved by using weighted least-squares estimation, instead of least-squares estimation in both of the parameter estimation and data estimation steps. To address the mismatch issue of “geometric duality” during parameter estimation, the residuals (prediction errors) are weighted according to the geometric similarity between the pixel of interest and the residuals. The robustness of data estimation can be improved by modeling the weights of residuals with the well-known bilateral filter. Experimental results show that there is a 0.25-dB increase in peak signal-to-noise ratio (PSNR) for a sample set of natural images after the suggested improvements are incorporated into the original SAI. The proposed algorithm produces the highest quality in terms of PSNR and subjective quality among sophisticated algorithms in the literature. [ABSTRACT FROM AUTHOR]

This article concerns the estimation algorithms of battery's parameters and state of charge (SOC) and it is twofold. First, we present a simple variable length block wise least square estimation algorithm by considering locally linear SOC and open circuit voltage (OCV) relation on the run. The proposed algorithm can estimate accurately the parameters and follow the parameter changes. After estimating the battery parameters, SOC is directly computed from the combination of estimated parameters and SOC-OCV relation. This estimation scheme, which assumes hysteresis effect is negligible, is an online method. Second, if the hysteresis effect is considered in the model, we present an additional least square optimization problem to estimate SOC accurately. In this case, some parameters are tuned up in an offline way. Our approach compared with the extended Kalman filter based estimation method. The algorithms are validated by experiments with real data obtained from lab tests. [ABSTRACT FROM AUTHOR]

The present study deals with the analysis of virus transport in groundwater and estimation of transport parameters. The parameter estimation is formulated as a least-squares minimization problem in which the parameters are estimated by minimizing the deviations between the model predicted and experimentally observed virus concentrations. A parameter estimation procedure is developed by coupling a hybrid finite volume numerical model simulating one-dimensional virus transport with the Levenberg-Marquadart algorithm. The numerical model employs a globally second order accurate explicit finite volume method for the advective transport and an implicit finite difference method for the dispersive transport. The comparison of model prediction with analytical solution shows that the numerical model accurately predicts virus movement in both advective and dispersive dominated transport. The parameter estimation indicates that for the case of estimating more than three unknown parameters, the inverse procedure results in nonunique optimal estimates. Further, for the case of estimating two or three unknown parameters, the presence of inactivation coefficients of liquid and sorbed phases also results in nonunique estimates. It is concluded that a priori estimation of one of the inactivation coefficient is necessary for unique estimation of other unknown parameters. A detailed statistical analysis is carried out to study the effect of bias induced by the objective function on the estimated parameters when the data contains the errors. It is found that in the presence of noise in the virus concentration data, the objective function does not induce any bias on the parameter estimates as the true value falls within 95% confidence levels at all noise levels. [ABSTRACT FROM AUTHOR]