Design of Digital Constrained Linear Least-Squares Multiple-Resonator-Based Harmonic Filtering

Efficient Digital Pre-filtering for Least-Squares Linear Approximation

In this paper we propose a very simple FIR pre-filter based method for near optimal least-squares linear approximation of discrete time signals. A digital pre-processing filter, which we demonstrate to be near-optimal, is applied to the signal before performing the usual linear interpolation. This leads to a non interpolating reconstruction of the signal, with good reconstruction quality and ve...

Least Squares Filtering

A general estimation model is defined in which two observations are available; one being a noisy version of the transmitted signal, while the other is a noisy-filtered and delayed version of the same transmitted signal. The delay and the filter are unknown quantities that must be estimated. An adaptive system, based on the least squares (LS) estimation criterion, is proposed in order to perform...

Design of Stable IIR Digital Filters With Equiripple Passbands and Peak-Constrained Least-Squares Stopbands

This paper describes an algorithmic development for the design of stable infinite impulse response (IIR) digital filters with equiripple passbands and peak-constrained leastsquares stopbands. Central to the development is a re-formulation of the design problem as an iterative quadratic programming problem where the stability and equiripple passbands and peakconstrained requirements are met by i...

Least Squares Solutions of Inconsistent Fuzzy Linear Matrix Equations

Linearized Alternating Direction Method for Constrained Linear Least-squares Problem

In this paper, we apply the alternating direction method (ADM) to solve a constrained linear least-squares problem where the objective function is a sum of two least-squares terms and the constraints are box constraints. Using ADM, we decompose the original problem into two easier least-squares subproblems at each iteration. To speed up the inner iteration, we linearize the subproblems whenever...