Article
Automation & Control Systems
Rodrigo A. Ricco, Bruno O. S. Teixeira
Summary: In this paper, we discuss the advantages of considering active constraints in modeling dynamic systems, and address the issues in constrained discrete-time state-space estimation. By rewriting state equality constraints and vectorizing the least squares problem, any method from the equality-constrained least squares framework can be applied for modeling state-space systems. Time-invariant and time-varying scenarios, as well as cases where the state equality constraint is not fully known, are considered.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2022)
Article
Engineering, Multidisciplinary
Baolei Wei
Summary: Parameter estimation is a crucial step in grey system models for time series modeling and forecasting. This study presents a separable grey system model that encompasses both linear and nonlinear models with separable structural parameters. Three least squares-based strategies are proposed for estimating structural parameters and initial conditions. Nonlinear least squares outperforms the other two strategies, especially in scenarios with large time intervals and high noise levels. Real-world applications demonstrate the effectiveness of the proposed method in forecasting failure times of products and traffic flows.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Mathematics
Zhijun Li, Minxing Sun, Qianwen Duan, Yao Mao
Summary: This study proposes a novel robust estimation method that combines Kalman filter and state augmentation to address the simultaneous existence of random model parametric uncertainties and constant measurement delay in discrete-time linear systems. The method is theoretically analyzed and numerically simulated, showing better processing capability for measurement delay and better robustness to model parametric uncertainties compared to the Kalman filter based on nominal parameters.
Article
Automation & Control Systems
Haoming Xing, Feng Ding, Feng Pan, Erfu Yang
Summary: This article proposes an identification model for MIMO systems by decomposing them into multiple-input single-output subsystems, and derives corresponding identification algorithms based on auxiliary model identification. To improve computational efficiency, a hierarchical identification model and algorithm are also proposed. Simulation examples show the effectiveness of the studied algorithms.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2023)
Article
Mathematics
Juan R. A. Bobenrieth, Eugenio S. A. Bobenrieth, Andres F. Villegas, Brian D. Wright
Summary: This paper introduces three key features of standard dynamic volatility models and presents a novel method for proof of consistency and asymptotic normality, laying a foundation for estimation and hypothesis testing of nonstationary models without detrending.
Article
Optics
Yong -Mei Li, Hai -Ling Liu, Shi-Jie Pan, Su-Juan Qin, Fei Gao, Dong-Xu Sun, Qiao-Yan Wen
Summary: This paper proposes a complete quantum algorithm for the k-medoids algorithm, which utilizes quantum subroutines to improve the speed of cluster assignment and center update. Compared to existing algorithms, our quantum k-medoids algorithm achieves a polynomial speedup in large data sets.
Review
Engineering, Mechanical
Randall J. Allemang, Rohit S. Patwardhan, Murali M. Kolluri, Allyn W. Phillips
Summary: This paper outlines various FRF estimation techniques and compares algorithms that compute FRF using different methods. It also discusses inconsistencies in some conditioned coherence metrics and provides corrected interpretations.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Mathematics, Applied
Baolei Wei, Naiming Xie
Summary: This study investigates the parameter estimation of grey system models from noisy observations, and finds that nonlinear least squares has multiple advantages over the conventional integral matching method in terms of accuracy and robustness.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2021)
Article
Computer Science, Artificial Intelligence
Liang-Qun Li, Xi-yang Zhan, Wei-Xin Xie, Zong-Xiang Liu
Summary: This paper presents an interacting T-S fuzzy semantic model estimator (ITS-FSM) for maneuvering target tracking, which integrates a probabilistic switching model and an efficient maximum entropy fuzzy clustering method to achieve accurate estimation. The proposed algorithm demonstrates effectiveness in handling non-Gaussian noise based on experiments on three simulation datasets.
Article
Automation & Control Systems
Haoming Xing, Feng Ding, Feng Pan
Summary: This article studies an auxiliary model least squares iterative (AM-LSI) algorithm for MIMO systems, and proposes a new auxiliary model hierarchical least squares iterative (AM-HLSI) algorithm for MIMO systems. The AM-HLSI algorithm has higher computational efficiency than the AM-LSI algorithm, and their feasibility is validated by a simulation example.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Energy & Fuels
Zhuo Wang, Daniel T. Gladwin, Matthew J. Smith, Stefan Haass
Summary: This study demonstrates how cell-level state estimation techniques can be used to achieve accurate SOC estimation on large-scale BESSs, and how parameters of DSPKF can be optimized using a genetic algorithm. The results show that using DSPKF for SOC estimation provides more accurate results compared to commercial BESS battery management systems, and when combined with TLS method, capacity estimation error can be reduced to less than 1%.
Article
Mathematics, Applied
Feng Ding
Summary: Least squares is an important method used for solving linear fitting and quadratic optimization problems. This paper explores the properties of least squares methods and multi-innovation least squares methods, and demonstrates important contributions in the area of system identification such as auxiliary model identification, multi-innovation identification theory, hierarchical identification principle, coupling identification concept, and filtering identification idea. The results of least squares and multi-innovation least squares algorithms for linear regressive systems with white noises can be extended to systems with colored noises.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
(2023)
Article
Economics
Tianxiao Pang, Lingjie Du, Terence Tai-Leung Chong
Summary: This study focuses on the estimation of nonstationary multiple-break autoregressive models, showing that the duration of a break does not determine its identification order, but rather depends on the stochastic order of signal strength. The method is applied to existing models, with proposed estimation procedures and asymptotic theory.
JOURNAL OF ECONOMETRICS
(2021)
Article
Mathematics, Applied
Baasansuren Jadamba, Akhtar A. Khan, Fabio Raciti, Miguel Sama
Summary: This paper develops a stochastic approximation approach for estimating the flexural rigidity within the framework of variational inequalities. The nonlinear inverse problem is analyzed as a stochastic optimization problem using an energy least-squares formulation. A stochastic variational inequality is solved by a stochastic auxiliary problem principle-based iterative scheme, which satisfies the necessary and sufficient optimality condition for the optimization problem. The convergence analysis for the proposed iterative scheme is given under general conditions on the random noise. Detailed computational results demonstrate the feasibility and efficacy of the proposed methodology.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2022)
Article
Agriculture, Multidisciplinary
Dongxue Zhao, Maryem Arshad, Jie Wang, John Triantafilis
Summary: The study aims to determine the best model for predicting topsoil exchangeable calcium, magnesium, potassium, and sodium; evaluate the applicability of the best topsoil model for subsurface and subsoil exchangeable cations; explore the effect of spiking on subsurface and subsoil prediction using the topsoil spectral library; and assess if building a profile spectral library with all depths improves prediction. PLSR was superior for predicting topsoil exchangeable cations, while Cubist outperformed PLSR in certain cases when spiking was applied and a profile spectral library was considered. Topsoil PLSR could also be used to predict subsurface and subsoil exchangeable calcium and magnesium, with spiking improving prediction. Profile spectral library achieved equivalent results when considering topsoil samples coupled with spiking.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Engineering, Electrical & Electronic
Alam Abbas Syed, Hassan Foroosh
Summary: This paper presents effective methods using spherical polar Fourier transform data for two different applications: 3D volumetric registration and machine learning classification network. The proposed method for registration offers unique and effective techniques, handling arbitrary large rotation angles and showing robustness. The modified classification network achieves robust classification results in processing spherical data.
Article
Engineering, Electrical & Electronic
Ruibo Fan, Mingli Jing, Jingang Shi, Lan Li, Zizhao Wang
Summary: In this study, a new low-rank sparse decomposition algorithm named TVRPCA+ is proposed for foreground-background separation. The algorithm combines spectral norm, structured sparse norm, and total variation regularization to suppress noise and obtain cleaner foregrounds. Experimental results demonstrate that TVRPCA+ achieves high performance in complex backgrounds and noise scenarios.
Article
Engineering, Electrical & Electronic
Omair Aldimashki, Ahmet Serbes
Summary: This paper proposes a coarse-to-fine FrFT-based algorithm for chirp-rate estimation of multi-component LFM signals, which achieves improved performance and a reduced signal-to-noise breakdown threshold by utilizing mathematical models for coarse estimation and a refined estimate-and-subtract strategy. Extensive simulation results demonstrate that the proposed algorithm performs very close to the Cramer-Rao lower bound, with the advantages of eliminating leakage effect, avoiding error propagation, and maintaining acceptable computational cost compared to other state-of-the-art methods.
Article
Engineering, Electrical & Electronic
Xinlei Shi, Xiaofei Zhang, Yuxin Sun, Yang Qian, Jinke Cao
Summary: In this paper, a low-complexity localization approach for multiple sources using two-dimensional discrete Fourier transform (2D-DFT) is proposed. The method computes the cross-covariance and utilizes phase offset method and total least square solution to obtain accurate position estimates.
Article
Engineering, Electrical & Electronic
Prabhanjan Mannari, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan
Summary: This paper discusses the problem of extended target tracking for a single 2D extended target with a known convex polytope shape and dynamics. It proposes a framework based on the existing point multitarget tracking framework to address the challenges of uncertainty in shape and kinematics, as well as self-occlusion. The algorithm developed using this framework is capable of dynamically changing the number of parameters used to describe the shape and estimating the whole target shape even when different parts of the target are visible at different frames.
Article
Engineering, Electrical & Electronic
Yongsong Li, Zhengzhou Li, Jie Li, Junchao Yang, Abubakar Siddique
Summary: This paper proposes a weighted adaptive ring top-hat transformation (WARTH) for extracting infrared small targets in complex backgrounds. The WARTH method effectively measures local and global feature information using an adaptive ring-shaped structural element and a target awareness indicator, resulting in accurate detection of small targets with minimized false alarms.
Article
Engineering, Electrical & Electronic
Yu Wang, Zhen Qin, Jun Tao, Yili Xia
Summary: In this paper, an enhanced sparsity-aware recursive least squares (RLS) algorithm is proposed, which combines the proportionate updating (PU) and zero-attracting (ZA) mechanisms, and introduces a general convex regularization (CR) function and variable step-size (VSS) technique to improve performance.
Article
Engineering, Electrical & Electronic
Neil J. Bershad, Jose C. M. Bermudez
Summary: This paper analyzes the impact of processing delay on the Least Mean Squares (LMS) algorithm in system identification, highlighting bias issues in the resulting weight vector.
Article
Engineering, Electrical & Electronic
Kanghui Jiang, Defu Jiang, Mingxing Fu, Yan Han, Song Wang, Chao Zhang, Jingyu Shi
Summary: In this paper, a novel method for velocity estimation using multicarrier signals in a single dwell is proposed, which effectively addresses the issue of Doppler ambiguity in pulse Doppler radars.
Article
Engineering, Electrical & Electronic
Xiao-Jun Zhang, Peng-Lang Shui, Yu-Fan Xue
Summary: This paper proposes a method for low-velocity small target detection in maritime surveillance radars. It models sea clutter sequences using the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and inverse Gamma distributed texture. The proposed detector, which is a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD), shows competitive detection performance in experiments.
Article
Engineering, Electrical & Electronic
Aiyi Zhang, Fulai Liu, Ruiyan Du
Summary: This paper proposes an adaptive weighted robust data recovery method with total variation regularization for hyperspectral image. The method models the HSI recovery problem as a tensor robust principal component analysis optimization problem, decomposing the data into low-rank HSI data, outliers, and noise component. An adaptive weighted strategy is then defined to impose on the tensor nuclear norm and outliers, using the priori information of singular values and strengthening the sparsity of outliers.
Article
Engineering, Electrical & Electronic
Hamid Asadi, Babak Seyfe
Summary: This paper presents a novel approach for estimating the model order in the presence of observation errors. The proposed method is based on correntropy estimation of eigenvalues in the observation space, which is further enhanced by resampling the observations using the bootstrap method. The algorithm partitions the observation space into signal and noise subspaces using the covariance matrix of mixtures, and determines the model order based on a correntropy estimator with kernel functions. Theoretical analysis and comparative evaluations demonstrate the superiority of this information-theoretic approach.
Article
Engineering, Electrical & Electronic
Buket colak Guvenc, Engin Cemal Menguc
Summary: In this paper, a novel family of online censoring based complex-valued least mean kurtosis (CLMK) algorithms is proposed. The algorithms censor less informative complex-valued data streams and reduce the costs of data processing without affecting accuracy. Robust algorithms are also developed to handle outliers. The simulation results confirm the attractive features of the proposed algorithms in large-scale system identification and regression scenarios.
Article
Engineering, Electrical & Electronic
Yun Su, Weixian Tan, Yifan Dong, Wei Xu, Pingping Huang, Jianxin Zhang, Diankun Zhang
Summary: In this study, a novel method for detecting low-resolution and small targets in millimeter wave radar images is proposed. The Wavelet-Conv structure and Wavelet-Attention mechanism are introduced to overcome the limitations of existing detectors. Experimental results demonstrate that the proposed method improves recall and mean average precision while maintaining competitive inference speed.
Article
Engineering, Electrical & Electronic
Xin Wang, Xingxing Jiang, Qiuyu Song, Jie Liu, Jianfeng Guo, Zhongkui Zhu
Summary: This study proposes a variational mode extraction (VME) method for extracting specific modes from complicated signals. By exploring the convergence property of VME, strategies for identifying ICF and determining the balance parameter are designed, and a bandwidth estimation strategy is constructed. The effectiveness of the proposed method for bearings fault diagnosis is verified and compared with other methods.