UWB has the characteristics of low power consumption, system complexity, strong anti-interference, high penetration, and positioning accuracy. In this paper, principle ultra-wide-band ranging is introduced in detail, followed by TOA method based on two-way time-of-flight ranging, results are solved using least-squares Gauss-Newton iterative algorithms, experimental The errors were analyzed. sho...

During the analysis of statistical data, one of the most important steps is the estimation of the considered parameters model. The most common estimation methods are the maximum likelihood and the least squares. When the data are considered normal, there is equivalence between the two methods, so there is no privilege for one or the other method. However, if the data are not Gaussian, this equi...

We present a structured algorithm for solving constrained nonlinear least squares problems, and establish its local two-step Q-superlinear convergence. The approach is based on an adaptive structured scheme due to Mahdavi-Amiri and Bartels of the exact penalty method of Coleman and Conn for nonlinearly constrained optimization problems. The structured adaptation also makes use of the ideas of N...

Interior point methods for semideenite optimization (SDO) have recently been studied intensively, due to their polynomial complexity and practical eeciency. Most of these methods are extensions of linear optimization (LO) algorithms. Unlike in the LO case, there are several diierent ways of constructing primal-dual search directions in SDO. The usual scheme is to apply linearization in conjunct...

A regularized Newton-like method for solving nonnegative least-squares problems is proposed and analysed in this paper. A preconditioner for KKT systems arising in the method is introduced and spectral properties of the preconditioned matrix are analysed. A bound on the condition number of the preconditioned matrix is provided. The bound does not depend on the interior-point scaling matrix. Pre...

We solve the V-V-P, vorticity-velocity-pressure, formulation of the stationary incompressible Navier-Stokes equations based on the least-squares finite element method. For the discrete systems, we use a conjugate gradient (CG) solver accelerated with a geometric multigrid preconditioner for the complete system. In addition, we employ a Krylov space smoother inside of the multigrid which allows ...

In this paper we propose a modified Newton-Raphson method to obtain super efficient frequency estimators of sinusoidal signals in presence of stationary noise. It is observed that if we start from an initial estimator with convergence rate Op(n ) and use the NewtonRaphson algorithm with proper step factor modification, then it produces super efficient frequency estimator in the sense it has asy...

This paper proposes new approximate QR-based algorithms for recursive nonlinear least squares (NLS) estimation. Two QR decomposition-based recursive algorithms are introduced based on the classical Gauss-Newton (GN) and Levenberg-Marquardt (LM) algorithms in nonlinear unconstrained optimization or least squares problems. Instead of using the matrix inversion formula, recursive QR decomposition ...

We present regularization tools for training small, medium and large feed-forward artiicial neural networks. The determination of the weights leads to very ill-conditioned nonlinear least squares problems and regularization is often suggested to get control over the network complexity, small variance error, and to get a nice optimization problem. The algorithms proposed explicitly use a sequenc...

When GPS signal measurements have outliers, using least squares (LS) estimation will likely give poor position estimates. One of typical approaches to handling this problem is to use robust estimation techniques. In this paper, we study the computational issues of Huber’s M-estimation applied to relative positioning. First for code based relative positioning, we use simulation results to show N...