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Error Correction of Magnetic Gradient Tensor Localization for UXO-Like Targets With Portable Transient Electromagnetic Systems

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IEEE SENSORS JOURNAL 2025年第3期25卷 5327-5336页

作者： Wang, Lijie Chen, Shudong China Jiliang Univ Coll Mech & Elect Engn Hangzhou 130018 Peoples R China Jilin Univ Coll Elect Sci & Engn Changchun 130012 Peoples R China

The magnetic gradient tensor (MGT) algorithm, which can quickly locate underground targets without iteration, is constrained by positioning errors when detecting unexploded ordnance (UXO) with a transient electromagnetic (TEM) system due to the large sensor baseline distance. An innovative method based on a cross-shaped portable TEM system is proposed to improve the positioning accuracy of MGT localization. First, the MGT algorithm is introduced to estimate target positions and positioning errors. Second, an error correction model is constructed, with its parameters fit using the least squares algorithm. Finally, the estimated target position obtained from the MGT algorithm is corrected based on the error correction model. Experimental results show that the proposed method can notably improve the positioning accuracy of underground targets, achieving a horizontal position error of no more than 10 cm and a depth position error of no more than 5 cm. The inversion time takes approximately 25 ms, providing a novel method for real-time detection and rapid positioning of UXO near the surface.

关键词： Error correction model least squares algorithm magnetic gradient tensor (MGT) transient electromagnetic (TEM) unexploded ordnance (UXO)

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Frequency Tracking and Phasor Estimation Using least squares and Total least squares algorithms

Frequency Tracking and Phasor Estimation Using Least Squares...

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作者： Guo, Hengdao University of Kentucky

学位级别：master

System stability plays an important role in electric power systems. With the development of electric power system, the scale of the electric grid is now becoming larger and larger, and many renewable energy resources are integrated in the grid. However, at the same time, the stability and safety issues of electric power system are becoming more complicated. Frequency and phasors are two critical parameters of the system stability. Obtaining these two parameters have been great challenges for decades. Researchers have provided various kinds of algorithms for frequency tracking and phasor estimation. Among them, least squares (LS) algorithm is one of the most commonly used algorithm. This thesis studies the LS algorithm and the Total least squares (TLS) algorithm working on frequency tracking and phasor estimation. In order to test the performance of the two algorithms, some simulations have been made in the Matlab. The Total Vector Error (TVE) is a commonly used performance criteria, and the TVE results of the two algorithms are compared. The TLS algorithm performs better than LS algorithm when the frequencies of all harmonic components are given

关键词： Frequency Tracking Phasor Estimation least squares algorithm Total least squares algorithm Decaying D.C. Component Electrical and Computer Engineering

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Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第12期38卷 15392-15399页

作者： Yu, Lean Yao, Xiao Wang, Shouyang Lai, K. K. Chinese Acad Sci Acad Math & Syst Sci Inst Syst Sci MADIS Beijing 100190 Peoples R China Hangzhou Normal Univ Hangzhou Key Lab E Business & Informat Secur Hangzhou 310036 Zhejiang Peoples R China City Univ Hong Kong Dept Management Sci Kowloon Hong Kong Peoples R China

Support vector machines (SVM) is proved to be one of the most effective tool in credit risk evaluation. However, the performance of SVM is sensitive not only to the algorithm for solving the quadratic programming but also to the parameters setting in its learning machines as well as to the importance of different classes. In order to solve these issues, this paper proposes a weighted least squares support vector machine (LSSVM) classifier with design of experiment (DOE) for parameter selection for credit risk evaluation. In this approach, least squares algorithm is used to solve the quadratic programming, the DOE is used for parameter selection in SVM modelling and weights in LSSVM are used to emphasize the importance of difference classes. For illustration purpose, two publicly available credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed weighted LSSVM classifier. The results show that the proposed weighted LSSVM classifier with DOE can produce the promising classification results in credit risk evaluation, relative to other classifiers listed in this study. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Credit risk evaluation Weighted LSSVM classifier least squares algorithm Design of experiment Parameter selection

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Reflection type Q-factor measurement using standard least squares methods

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IET MICROWAVES ANTENNAS & PROPAGATION 2011年第4期5卷 426-432页

作者： Shahid, S. Ball, J. A. R. Wells, C. G. Wen, P. Univ So Queensland Fac Engn & Surveying Toowoomba Qld 4350 Australia

Methods to determine resonant cavity Q-factor from vector network analyser reflection data are often quite complex. This study presents a simple method which makes use of two least squares algorithms applied sequentially. Firstly, a circle is fitted to the data, which is then projected onto a linear frequency scale using the Ginzton technique. A straight line is fitted to the projected data, and the loaded Q-factor and the resonant frequency are obtained from the line coefficients. The method has been tested on both synthetic and experimental data, and compared with the Kajfez QZERO software, and the results are shown to be accurate to a fraction of 1%.

关键词： Q-factor measurement least squares algorithm straight line line coefficients Interpolation and function approximation (numerical analysis) reflection type Q-factor measurement resonant frequency vector network analyser reflection data loaded Q-factor least squares approximations Ginzton technique linear frequency scale resonant cavity Q-factor Other electric variables measurement Kajfez QZERO software standard least squares method

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Design and experimental evaluation of improved least squares and weighted least squares quadrature mirror filters

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2006年第1期E89A卷 310-315页

作者： Vinod, AP Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

The least squares (LS) and the weighted least squares (WLS) algorithms are well known procedures that are used in the design of quadrature mirror filters (QMFs). It is known that these design techniques suffer from pass-band anomaly under certain conditions. A recent method, that overcomes pass-band anomaly for LS QMFs using a frequency sampling design for the initial filter, is extended to WLS design in this letter. A comparison between the modified LS and WLS designs based on experimental results is presented. Although WLS designs have been reported to have superior near-equiripple stop-band performance as compared to LS designs, we find that this is not always true. Specifically, LS designs, with inherent computational and robustness advantages, also have better peak stop-band ripple and transition bandwidth at higher cut-off frequencies than WLS.

关键词： quadrature mirror filter least squares algorithm weighted least squares algorithm frequency response

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Multistage least squares based iterative estimation for feedback nonlinear systems with moving average noises using the hierarchical identification principle

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NONLINEAR DYNAMICS 2013年第1-2期73卷 583-592页

作者： Hu, Peipei Ding, Feng Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Jiangnan Univ Control Sci & Engn Res Ctr Wuxi 214122 Peoples R China

This paper develops a multistage least squares based iterative algorithm to estimate the parameters of feedback nonlinear systems with moving average noise from input-output data. Since that the identification model is bilinear on the unknown parameter space, the solution is to decompose a system into several subsystems with each of which is linear about its parameter vector, then to replace the unknown noise terms in the information vectors with their corresponding estimates at the previous iteration of each subsystem, and estimate each subsystem, respectively. The simulation results show that the proposed algorithm can work well.

关键词： Parameter estimation Iterative identification least squares algorithm Hierarchical identification principle Nonlinear system Feedback system

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The posterior selection method for hyperparameters in regularized least squares method

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Control Theory and Technology 2024年第2期22卷 184-194页

作者： Yanxin Zhang Jing Chen Yawen Mao Quanmin Zhu School of Science Jiangnan UniversityWuxi214122JiangsuChina The Science and Technology on Near-Surface Detection Laboratory Wuxi214122JiangsuChina Department of Engineering Design and Mathematics University of the West of EnglandBristolBS161QYUK

The selection of hyperparameters in regularized least squares plays an important role in large-scale system identification. The traditional methods for selecting hyperparameters are based on experience or marginal likelihood maximization method, which are inaccurate or computationally expensive. In this paper, two posterior methods are proposed to select hyperparameters based on different prior knowledge (constraints), which can obtain the optimal hyperparameters using the optimization theory. Moreover, we also give the theoretical optimal constraints, and verify its effectiveness. Numerical simulation shows that the hyperparameters and parameter vector estimate obtained by the proposed methods are the optimal ones.

关键词： Regularization method Hyperparameter System identification least squares algorithm

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On the Accuracy of least squares algorithms for Estimating Zeros

On the Accuracy of Least Squares Algorithms for Estimating Z...

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American Control Conference

作者： Matthew S. Fledderjohn Matthew S. Holzel Alexey V. Morozov Dennis S. Bernstein University of Michigan Ann Arbor MI Department of Aerospace Engineering The University of Michigan Ann Arbor MI

ISBN: (纸本)9781424474264

We investigate the accuracy of least-squares-based algorithms for estimating system zeros in the presence of known or unknown order and known or unknown relative degree. Specifically, we use least-squares to estimate the parameters of ARX and mu-Markov models from which zero estimates are calculated directly using the numerator polynomial as well as indirectly using the truncated Laurent expansion or the eigensystem realization algorithm (ERA). To employ the truncated Laurent expansion or ERA, we consider the Markov parameters estimated from the mu-Markov model. Lastly, we investigate the spurious zeros of the mu-Markov model and truncated Laurent expansion to determine to what extent these zeros behave in a predictable manner.

关键词： Zero least squares algorithm

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A Comparison of least squares algorithms for Estimating Markov Parameters

A Comparison of Least Squares Algorithms for Estimating Mark...

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American Control Conference

作者： Matthew S. Fledderjohn Matthew S. Holzel Harish J. Palanthandalam-Madapusi Robert J. Fuentes Dennis S. Bernstein University of Michigan Ann Arbor MI Deptartment of Mechanical and Aerospace Engineering Syracuse University Syracuse NY Flight Controls Department Raytheon Missle Systems Tucson AZ Deptartment of Aerospace Engineering University of Michigan Ann Arbor MI

ISBN: (纸本)9781424474264

The purpose of this work is to compare model structures and identification algorithms for estimating Markov parameters in the presence of uncorrelated and correlated input, process, and output noise. We consider several least-squares variants with ARX and mu-Markov model structures, which are compared with white noise identification signals.

关键词： least squares algorithm input process output noise Markov Model structures

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A least squares Method for Identification of Feedback Cascade Systems

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IFAC-PapersOnLine 2015年第28期48卷 98-103页

作者： Galrinho, Miguel Rojas, Cristian R. Hjalmarsson, Håkan Department of Automatic Control and ACCESS Linnaeus Center School of Electrical Engineering KTH - Royal Institute of Technology SE-100 44 Stockholm Sweden

The problem of identification of systems in dynamic networks is considered. Although the prediction error method (PEM) can be applied to the overall system, the non-standardmodel structure requires solving a non-convex optimization problem. Alternative methods have been proposed, such as instrumental variables and indirect PEM. In this paper, we first consider acyclic cascade systems, and argue that these methods have different ranges of applicability. Then, for a network with feedback connection, we propose an approach to deal with the fact that indirect PEM yields a non-convex problem in that case. A numerical simulation may indicate that this approach is competitive with other existing methods. © 2015

关键词： least squares approximations Convex optimization Error analysis Feedback Identification (control systems) Numerical methods Closed loop identification Feed back loop Feed forward network Identification of systems Instrumental variables least squares algorithm Nonconvex optimization Prediction error method

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