Efficient Localization Algorithm for Non-Linear Least Square Estimation
نویسندگان
چکیده
منابع مشابه
Using an Efficient Penalty Method for Solving Linear Least Square Problem with Nonlinear Constraints
In this paper, we use a penalty method for solving the linear least squares problem with nonlinear constraints. In each iteration of penalty methods for solving the problem, the calculation of projected Hessian matrix is required. Given that the objective function is linear least squares, projected Hessian matrix of the penalty function consists of two parts that the exact amount of a part of i...
متن کاملA Distributed Algorithm for Least Square Solutions of Linear Equations
A distributed discrete-time algorithm is proposed for multi-agent networks to achieve a common least squares solution of a group of linear equations, in which each agent only knows some of the equations and is only able to receive information from its nearby neighbors. For fixed, connected, and undirected networks, the proposed discrete-time algorithm results in each agents solution estimate to...
متن کاملResearch on the Least Square Source Localization Algorithm ⋆
Localization algorithm based on the linear least square principle has the advantages of small amount of calculations, and attracts many researchers. In this paper, some algorithms based on linear least square estimation for single target localization are researched. We analyze the essence of these algorithms, give the specific estimation process and summarize the advantages and drawbacks of the...
متن کاملLeast Mean Square Algorithm
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...
متن کاملLeast Mean Square Algorithm
The Least Mean Square (LMS) algorithm, introduced by Widrow and Hoff in 1959 [12] is an adaptive algorithm, which uses a gradient-based method of steepest decent [10]. LMS algorithm uses the estimates of the gradient vector from the available data. LMS incorporates an iterative procedure that makes successive corrections to the weight vector in the direction of the negative of the gradient vect...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Journal of Korean Institute of Communications and Information Sciences
سال: 2015
ISSN: 1226-4717
DOI: 10.7840/kics.2015.40.1.88