检索结果-内蒙古大学图书馆

An accurate RSS/AoA-based localization method for internet of underwater things

AD HOC NETWORKS 2023年 145卷

作者： Pourkabirian, Azadeh Kooshki, Fereshteh Anisi, Mohammad Hossein Jindal, Anish Islamic Azad Univ Dept Comp & Informat Technol Engn Qazvin Branch Qazvin *** Iran Islamic Azad Univ Dept Math Qazvin Branch Qazvin *** Iran Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ England Univ Durham Dept Comp Sci Durham DH1 3LE England

Localization is an important issue for Internet of Underwater Things (IoUT) since the performance of a large number of underwater applications highly relies on the position information of underwater sensors. In this paper, we propose a hybrid localization approach based on angle-of-arrival (AoA) and received signal strength (RSS) for IoUT. We consider a smart fishing scenario in which using the proposed approach fishers can find fishes' loca-tions effectively. The proposed method collects the RSS observation and estimates the AoA based on error variance. To have a more realistic deployment, we assume that the perfect noise information is not available. Thus, a minimax approach is provided in order to optimize the worst-case performance and enhance the esti-mation accuracy under the unknown parameters. Furthermore, we analyze the mismatch of the proposed esti-mator using mean-square error (MSE). We then develop semidefinite programming (SDP) based method which relaxes the non-convex constraints into the convex constraints to solve the localization problem in an efficient way. Finally, the Cramer-Rao lower bounds (CRLBs) are derived to bound the performance of the RSS-based estimator. In comparison with other localization schemes, the proposed method increases localization accu-racy by more than 13%. Our method can localize 96% of sensor nodes with less than 5% positioning error when there exist 25% anchors.

关键词： Localization RSS AoA semidefinite programming Cramer -Rao lower bound Industrial Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Efficient semidefinite solutions for TDOA-based source localization under unknown PS

引用

PERVASIVE AND MOBILE COMPUTING 2023年 91卷

作者： Wu, Xiaoping Zhao, Li Zhu, Xuefen Huzhou Univ Sch Informat Engn Huzhou 313000 Peoples R China Zhejiang A&F Univ Informat & Educ Technol Ctr Hangzhou 311300 Peoples R China

Two efficient solutions via Semi-Definite programming (SDP) are proposed for source localization problems using time difference of arrival (TDOA)-based ranging measurements when the propagation speed (PS) is unavailable and considered as a variable. For this problem, we propose a relaxed SDP (RSDP) solution, the performance of which is suboptimal. Accordingly, we propose a two-stage SDP method to improve the performance by applying the rank-reduction method. Besides, we also propose a penalty function-based SDP (PF-SDP) by introducing the penalty term. By doing so, the cost function becomes tighter so that the solution performs better. The simulated results show that the performance of two-stage SDP is sufficiently close to the Cramer-Rao Lower Bound (CRLB) accuracy at high noise levels. The PF-SDP outperforms the two-stage SDP in the presence of low noise levels. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Source localization semidefinite programming Time difference of arrival Penalty function

来源：评论

学校读者我要写书评

暂无评论

semidefinite Relaxation Method for Target Localization by MIMO Radar Using Bistatic Ranges

引用

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2014年第8期000卷 1-6页

作者： Sun, Bin Chen, Haowen Wei, Xizhang Li, Xiang Natl Univ Def Technol Sch Elect Sci & Engn Changsha 410073 Hunan Peoples R China

We address the target localization problem by using bistatic range (BR) measurements in widely separated multiple-input multiple-output (MIMO) radar network. The BR information defines a set of elliptic equations from which the target location can be estimated. By applying the semidefinite relaxation (SDR), we transform the nonconvex BR-based localization problem into a convex semidefinite programming (SDP) problem, whose solution is guaranteed to be globally optimal without initial estimate. Moreover, we extend this method to robustly solve the localization problem in the presence of antenna position errors. Simulation results demonstrate that the proposed SDR method provides superior estimation performance over the existing method.

关键词： semidefinite programming MIMO systems BISTATIC radar NUMERICAL solutions to elliptic equations PROBLEM solving

来源：评论

学校读者我要写书评

暂无评论

Instances of computational optimal recovery: Refined approximability models

引用

JOURNAL OF COMPLEXITY 2021年 62卷 101503-101503页

作者： Foucart, Simon Texas A&M Univ College Stn TX 77843 USA

Models based on approximation capabilities have recently been studied in the context of Optimal Recovery. These models, however, are not compatible with overparametrization, since modeland data-consistent functions could then be unbounded. This drawback motivates the introduction of refined approximability models featuring an added boundedness condition. Thus, two new models are proposed in this article: one where the boundedness applies to the target functions (first type) and one where the boundedness applies to the approximants (second type). For both types of models, optimal maps for the recovery of linear functionals are first described on an abstract level before their efficient constructions are addressed. By exploiting techniques from semidefinite programming, these constructions are explicitly carried out on a common example involving polynomial subspaces of C[-1, 1]. (c) 2020 Elsevier Inc. All rights reserved.

关键词： Optimal recovery Approximability models semidefinite programming Overparametrization

来源：评论

学校读者我要写书评

暂无评论

Computing sparse Fourier sum of squares on finite abelian groups in quasi-linear time

引用

APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS 2024年 73卷

作者： Yang, Jianting Ye, Ke Zhi, Lihong CNRS CREATE 1 CREATE Way Singapore 138602 Singapore Univ Chinese Acad Sci Key Lab Math Mechanizat AMSS 55 Zhongguancun East Rd Beijing 100190 Peoples R China

The problem of verifying the nonnegativity of a function on a finite abelian group is a longstanding challenging problem. The basic representation theory of finite groups indicates that a function on a finite abelian group can be written as a linear combination of characters of irreducible representations of by ( ) = Sigma is an element of ( ) ( ) , where is the dual group of consisting of all characters of and ( ) is the Fourier coefficient of at is an element of . In this paper, we show that by performing the fast (inverse) Fourier transform, we are able to compute a sparse Fourier sum of squares (FSOS) certificate of on a finite abelian group with complexity that is quasi-linear in the order of and polynomial in the FSOS sparsity of . Moreover, for a nonnegative function on a finite abelian group and a subset subset of , we give a lower bound of the constant such that + admits an FSOS supported on . We demonstrate the efficiency of the proposed algorithm by numerical experiments on various abelian groups of orders up to 10 7 . As applications, we also solve some combinatorial optimization problems and the sum of Hermitian squares (SOHS) problem by sparse FSOS.

关键词： Abelian group Chordal graph Convex optimization Fast Fourier transform Fourier sum of squares Graph theory Quasi-linear algorithm semidefinite programming Sparse Gram matrices

来源：评论

学校读者我要写书评

暂无评论

Approximate Pythagoras numbers on *-algebras over C ?

引用

JOURNAL OF COMPLEXITY 2023年 74卷

作者： Abbasi, Paria Gribling, Sander Klingler, Andreas Netzer, Tim Univ Innsbruck Dept Math Innsbruck Austria Univ Paris IRIF Paris France Univ Innsbruck Inst Theoret Phys Innsbruck Austria

The Pythagoras number of a sum of squares is the shortest length among its sums of squares representations. In many algebras, for example real polynomial algebras in two or more variables, there exists no upper bound on the Pythagoras number for all sums of squares. In this paper, we study how Pythagoras numbers in *-algebras over C behave with respect to small perturbations of elements. More precisely, the approximate Pythagoras number of an element is the smallest Pythagoras number among all elements in its epsilon-ball. We show that these approximate Pythagoras numbers are often significantly smaller than their exact versions, and allow for (almost) dimension-independent upper bounds. Our results use low-rank approximations for Gram matrices of sums of squares and estimates for the operator norm of the Gram map. (c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Pythgoras number Sums of squares Approximation semidefinite programming

来源：评论

学校读者我要写书评

暂无评论

A Novel relaxation of the static output feedback problem for a class of plants

引用

AUTOMATICA 2023年第1期158卷

作者： August, Elias Reykjavik Univ Dept Engn Menntavegur 1 IS-102 Reykjavik Iceland

The static output feedback control problem is important, as it is concerned with the case when one cannot measure all state variables. It seeks to obtain a stabilising control under these conditions and is, often, difficult to solve. For a certain class of linear dynamical systems, we provide a novel approach to obtain a stabilising control gain matrix. The class of plants considered is of those, for which we can measure at least half of all state variables and where those measurements affect at least half of all state variables. Moreover, when we write the (linearly transformed) system matrix in block form, we require that either the two lower block matrices are nonsingular, or, if the inputs affect the measured state variables directly, at least the lower left one is nonsingular. We then show that we can determine the stabilising control gain matrix from the solution of a linear matrix inequality. Finally, we apply the approach to different benchmark problems, where it performs well, and confirm its good performance through additional numerical experiments. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： Static output feedback Stability semidefinite programming Bilinear matrix inequality Convex relaxation

来源：评论

学校读者我要写书评

暂无评论

Lower bound for the cost of connecting tree with given vertex degree sequence

引用

JOURNAL OF COMPLEX NETWORKS 2020年第2期8卷 cnz031-cnz031页

作者： Goubko, Mikhail Kuznetsov, Alexander RAS Dept Act Syst VA Trapeznikov Inst Control Sci Profsoyuznaya 65 Moscow 117997 Russia Univ Informat Technol & Management Rzeszow Dept Cognit Sci Ul Sucharskiego 2 PL-35225 Rzeszow Poland

The optimal connecting network problem generalizes many models of structure optimization known from the literature, including communication and transport network topology design, graph cut and graph clustering, etc. For the case of connecting trees with the given sequence of vertex degrees the cost of the optimal tree is shown to be bounded from below by the solution of a semidefinite optimization program with bilinear matrix inequality constraints, which is reduced to the solution of a series of convex programs with linear matrix inequality constraints. The proposed lower-bound estimate is used to construct several heuristic algorithms and to evaluate their quality on a variety of generated and real-life datasets.

关键词： optimal communication network generalized Wiener index origin-destination matrix semidefinite programming bilinear matrix inequality Mosek

来源：评论

学校读者我要写书评

暂无评论

RANK-SPARSITY INCOHERENCE FOR MATRIX DECOMPOSITION

引用

SIAM JOURNAL ON OPTIMIZATION 2011年第2期21卷 572-596页

作者： Chandrasekaran, Venkat Sanghavi, Sujay Parrilo, Pablo A. Willsky, Alan S. MIT Dept Elect Engn & Comp Sci Lab Informat & Decis Syst Cambridge MA 02139 USA Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components. Such a problem arises in a number of applications in model and system identification and is intractable to solve in general. In this paper we consider a convex optimization formulation to splitting the specified matrix into its components by minimizing a linear combination of the l(1) norm and the nuclear norm of the components. We develop a notion of rank-sparsity incoherence, expressed as an uncertainty principle between the sparsity pattern of a matrix and its row and column spaces, and we use it to characterize both fundamental identifiability as well as (deterministic) sufficient conditions for exact recovery. Our analysis is geometric in nature with the tangent spaces to the algebraic varieties of sparse and low-rank matrices playing a prominent role. When the sparse and low-rank matrices are drawn from certain natural random ensembles, we show that the sufficient conditions for exact recovery are satisfied with high probability. We conclude with simulation results on synthetic matrix decomposition problems.

关键词： matrix decomposition convex relaxation l(1) norm minimization nuclear norm minimization uncertainty principle semidefinite programming rank sparsity

来源：评论

学校读者我要写书评

暂无评论

SOS-SDP: An Exact Solver for Minimum Sum-of-Squares Clustering

引用

INFORMS JOURNAL ON COMPUTING 2022年第4期34卷 2144-2162页

作者： Piccialli, Veronica Sudoso, Antonio M. Wiegele, Angelika Univ Roma Tor Vergata I-00133 Rome RM Italy Univ Klagenfurt A-9020 Klagenfurt Austria

The minimum sum-of-squares clustering problem (MSSC) consists of partitioning n observations into k clusters in order to minimize the sum of squared distances from the points to the centroid of their cluster. In this paper, we propose an exact algorithm for the MSSC problem based on the branch- and-bound technique. The lower bound is computed by using a cutting-plane procedure in which valid inequalities are iteratively added to the Peng-Wei semidefinite programming (SDP) relaxation. The upper bound is computed with the constrained version of k-means in which the initial centroids are extracted from the solution of the SDP relaxation. In the branch-and-bound procedure, we incorporate instance-level must-link and cannot-link constraints to express knowledge about which data points should or should not be grouped together. We manage to reduce the size of the problem at each level, preserving the structure of the SDP problem itself. To the best of our knowledge, the obtained results show that the approach allows us to successfully solve, for the first time, real-world instances up to 4,000 data points.

关键词： clustering semidefinite programming branch and bound

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：