Frequency Estimation of Irregularly Sampled Data Using a Sparsity Constrained Weighted Least-Squares Approach

Least-squares parameter estimation for systems with irregularly missing data

This paper considers the problems of parameter identification and output estimation with possibly irregularly missing output data, using output error models. By means of an auxiliary model (or reference model) approach, we present a recursive least-squares algorithm to estimate the parameters of missing data systems, and establish convergence properties for the parameter and missing output esti...

Weighted Least Squares Estimation with Missing Data

Power System State Estimation Using Weighted Least Squares (WLS) and Regularized Weighted Least Squares(RWLS) Method

In this paper, a new formulation for power system state estimation is proposed. The formulation is based on regularized least squares method which uses the principle of Thikonov’s regularization to overcome the limitations of conventional state estimation methods. In this approach, the mathematical unfeasibility which results from the lack of measurements in case of ill-posed problems is elimin...

Instantaneous Frequency Estimation Using a Least Squares Time-Frequency Method

We present a method for estimating the instantaneous frequency of a signal. This method involves the calculation of a time–frequency energy density of the signal, then obtaining an instantaneous frequency estimate from this joint density. Time–frequency energy density is calculated as a least squares optimal combination of multi–window Gabor based evolutionary spectra. The optimal weights are o...

Constrained Least-Squares Density-Difference Estimation

We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure that first estimates two densities separately and then computes their difference. However, such a two-step procedure does not necessarily work well because the first step is performed without regard to the second step and thus a small error in the first stage can cause...