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Abstract: In this paper we provide a computational approach to the shape of curves which are rational in polar coordinates, i.e. which are defined by means of a parametrization where both , are rational functions. Our study includes theoretical aspects on the shape of these curves, and algorithmic results which eventually lead to an algorithm for plotting the “interesting parts” of the curve, i.e. the parts showing the main geometrical features. [Copyright &y& Elsevier]

Since system identification plays a crucial role in controlling systems, it is essential to have access to appropriate identification methods. In this paper, two novel identification methods are proposed for estimating Controlled Auto-Regressive Moving Average (CARMA) systems: the multi-innovation gradient-based iterative algorithm and the two-stage multi-innovation gradientbased iterative algorithm. Our primary objective is to estimate the unknown parameters of a tumor model using these methods. To evaluate the effectiveness of the proposed methods, various factors are considered, such as convergence rate and estimation error. By conducting simulations, the practical applicability and performance of the introduced algorithms are demonstrated. The obtained results are presented through tables and figures, providing a comprehensive analysis of the estimation outcomes. The multi-innovation gradient-based iterative algorithm and the twostage multi-innovation gradient-based iterative algorithm offer valuable contributions to the field of system identification, particularly in the context of CARMA systems. These methods offer an innovative approach to estimate the parameters of complex systems, specifically focusing on tumor models. The convergence rate and estimation error analysis highlight the reliability and accuracy of the proposed methods, indicating their potential for practical implementation. In conclusion, this paper presents novel identification methods for estimating CARMA systems in the context of tumor models. The proposed algorithms demonstrate promising results in terms of convergence rate and estimation accuracy. These findings contribute to the development of effective and reliable identification techniques, offering valuable insights for controlling and understanding complex systems. [ABSTRACT FROM AUTHOR]

Real-time DOA (direction of arrival) estimation of surface or underwater targets is of great significance to the research of marine environment and national security protection. When conducting real-time DOA estimation of underwater targets, it can be difficult to extract the prior characteristics of noise due to the complexity and variability of the marine environment. Therefore, the accuracy of target orientation in the absence of a known noise is significantly reduced, thereby presenting an additional challenge for the DOA estimation of the marine targets in real-time. Aiming at the problem of real-time DOA estimation of acoustic targets in complex environments, this paper applies the MEMS vector hydrophone with a small size and high sensitivity to sense the conditions of the ocean environment and change the structural parameters in the adaptive adjustments system itself to obtain the desired target signal, proposes a signal processing method when the prior characteristics of noise are unknown. Theoretical analysis and experimental verification show that the method can achieve accurate real-time DOA estimation of the target, achieve an error within 3.1° under the SNR (signal-to-noise ratio) of the X channel of −17 dB, and maintain a stable value when the SNR continues to decrease. The results show that this method has a very broad application prospect in the field of ocean monitoring. [ABSTRACT FROM AUTHOR]

This paper addresses the challenging task of guide wire navigation in cardiovascular interventions, focusing on the parameter estimation of a guide wire system using Ensemble Kalman Inversion (EKI) with a subsampling technique. The EKI uses an ensemble of particles to estimate the unknown quantities. However, since the data misfit has to be computed for each particle in each iteration, the EKI may become computationally infeasible in the case of high-dimensional data, e.g. high-resolution images. This issue can been addressed by randomised algorithms that utilize only a random subset of the data in each iteration. We introduce and analyse a subsampling technique for the EKI, which is based on a continuous-time representation of stochastic gradient methods and apply it to on the parameter estimation of our guide wire system. Numerical experiments with real data from a simplified test setting demonstrate the potential of the method. [ABSTRACT FROM AUTHOR]

In this paper we propose a new parameter estimator that ensures global exponential convergence of linear regression models requiring only the necessary assumption of identifiability of the regression equation, which we show is equivalent to interval excitation of the regressor vector. An extension to – separable and monotonic – nonlinear parameterisations is also given. The estimators are shown to be robust to additive measurement noise and – not necessarily slow-parameter variations. Moreover, a version of the estimator that is robust with respect to sinusoidal disturbances with unknown internal model is given. Simulation results that illustrate the performance of the estimator compared with other algorithms are given. [ABSTRACT FROM AUTHOR]

In this paper, aiming at the problems of slow estimation speed and low estimation precision of traditional fractional-order system (FOS) parameter estimation method, an improved Archimedes optimization algorithm (IAOA) is proposed to calculate the optimal value. By establishing the parameter estimation model and the cost function, the parameter estimation problem is formulated as an optimization problem. As opposed to the Archimedes optimization algorithm (AOA), the IAOA introduces three improvements: leadership behavior, levy flight behavior and a new adaptive strategy. This paper verifies the performance of the IAOA by selecting 10 classic test functions. IAOA is applied to the parameter estimation problem of fractional-order unified system to verify the accuracy and feasibility of the algorithm. The simulation results prove that the IAOA has better global optimization ability and estimation accuracy than the original algorithm. [ABSTRACT FROM AUTHOR]

High quality scholarship requires technical excellence but also connects the work to the primary references in the field. In this way, the reader advances their knowledge and gains perspective. The TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING supports these goals by recognizing the best paper chosen by the Associate Editors and reviewers. The winning paper was selected from 140 papers published by TSM in 2015. The winner is “An Algorithm of Multi-Subpopulation Parameters With Hybrid Estimation of Distribution for Semiconductor Scheduling With Constrained Waiting Time” by Hung-Kai Wang, Chen-Fu Chien, and Mitsuo Gen published in the August 2015 issue of the IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING (10.1109/TSM.2015.2439054). I congratulate the authors on their selection. [ABSTRACT FROM AUTHOR]

For the parameter estimation problem in research related to the fractional-order chaotic systems (FOCSs), a modified optimization algorithm based on Salp Swarm Algorithm (SSA) was developed in this paper. The proposed algorithm introduced several improvements on SSA: adding a grouping step, introducing "betrayal" behavior, and improving the update method of the followers. We applied multiple classical optimization algorithms to conduct the parameter estimation experiments on the fractional-order Lorenz chaotic system (Lorenz-FOCS) and the fractional-order Financial chaotic system (Financial-FOCS). In addition, we explored the impact of searching space on parameters estimation through experiments. The experimental results confirmed the feasibility of the modified Salp Swarm Algorithm (MSSA). The MSSA performed better than the SSA and other classical optimization algorithms in terms of the estimation accuracy and convergence rate. [ABSTRACT FROM AUTHOR]

This paper focuses on the joint estimation of parameters and time delays for multi-input systems that contain unknown input delays and colored noise. A greedy pursuit hierarchical iteration algorithm is proposed, which can reduce the estimation cost. Firstly, an over-parameterized approach is employed to construct a sparse system model of multi-input systems even in the absence of prior knowledge of time delays. Secondly, the hierarchical principle is applied to replace the unknown true noise items with their estimation values, and a greedy pursuit search based on compressed sensing is employed to find key parameters using limited sampled data. The greedy pursuit search can effectively reduce the scale of the system model and improve the identification efficiency. Then, the parameters and time delays can be estimated simultaneously while considering the known orders and found locations of key parameters by utilizing iterative methods with limited sampled data. Finally, some simulations are provided to illustrate the effectiveness of the presented algorithm in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we consider the matter of on-line clustering of hyperplanes within the presence of bounded noise. This is often a challenging problem that involves the segmentation of the data and the estimation of the hyperplanes parameters. The proposed algorithm consists in two successive steps. At whenever, the first step allows to assign the current data point to the most appropriate hyperplane and therefore the second step realizes an update of the parameters of the hyperplane that contains the data point. The second step springs from an Outer Bounding Ellipsoid type algorithm suitable for on-line parameters estimation within the presence of bounded noise. The performance of our algorithm is proven using synthetic and real data. [ABSTRACT FROM AUTHOR]

In order to improve the development effect of private education, this paper analyzes the current situation of private education combined with the data mining algorithm and explores the problems existing in the development of private education. Moreover, this paper combines the semi-parametric product estimation method with parameter estimation and applies the estimation method to model-assisted sampling estimation. This work enhances the estimate accuracy of the sample estimation and increases the field of application of the model while enhancing the classic generalized regression estimation. It also modifies the estimation accuracy on the basis of the linear assumption. The experimental study reveals that the data mining algorithm-based analysis approach for private education development provided in this work has a certain impact, and the development strategy of private education is assessed on this premise. [ABSTRACT FROM AUTHOR]

Target detection is a complex process that is important as an important module in computer vision applications. In particular, in many occasions where the real-time requirements are extremely high, it is very important to achieve fast and accurate detection of targets. But at this stage, there are still many problems in the research on rapid target detection, such as inefficiency and high is the first phase of automatic target recognition (ATR). For the performance of SAR image target detection, this paper proposes a CFAR fast detection algorithm based on Rayleigh. CFAR detection is divided into two steps: horizontal and vertical CFAR detection. The efficiency of parameter estimation is improved by the coincidence of adjacent point reference windows and the distribution characteristics of images. The algorithm in this paper combines the target variance characteristics to reduce the false alarm rate. The experiment was performed on the MSTAR dataset. Fast target detection algorithm based on CFAR and target variance feature has the characteristics of high detection rate, low false alarm, and high speed, and its detection performance is good. The experimental results show that the recognition efficiency of the proposed algorithm is higher than that of the traditional algorithm on different target datasets, the time is shortened by 30%, and the accuracy rate is equal to that of the traditional algorithm. [ABSTRACT FROM AUTHOR]

This paper considers the identification problem of the linear continuous time-delay systems. By using the multi-frequency responses, a stochastic gradient gradient-based iterative (SG-GI) algorithm is derived. The proposed algorithm can estimate the unknown parameters and the unknown time delays simultaneously. To improve the parameter estimation accuracy of the SG-GI algorithm, a multi-innovation stochastic gradient gradient-based iterative (MISG-GI) algorithm is derived by using the multi-innovation identification theory. In addition, a forgetting factor is introduced to increase the parameter estimation accuracy. The resulting algorithm is called the multi-innovation forgetting gradient gradient-based iterative (MIFG-GI) algorithm. The effectiveness of the proposed strategies is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

Although there are several diagnostic modalities for tuberculous pleurisy, there is still a lack of easy, cost-effective, and rapid methods for confirming the diagnosis. In order to facilitate clinicians to diagnose patients with tuberculous pleurisy at an early stage, help patients to obtain treatment early, and reduce lung damage, it is hoped that new techniques will be available in the future to help diagnose tuberculous pleurisy rapidly in the clinic. To this end, this paper investigates the problem of bidirectional consistency based on event-triggered iterative learning. Firstly, a dynamic linearized data model of TB pleurisy intelligent system is established using compact-form dynamic linearization method, and a parameter estimation algorithm of TB pleurisy data model is proposed; then, based on this data model, an output observer and a dead zone controller are designed, and an event-triggered distributed model-free iterative learning bidirectional consistency control strategy is constructed by combining with signal graph theory. In this paper, 112 patients with pleural effusion were collected, including 76 patients with confirmed or clinically diagnosed tuberculous pleural effusion and 36 patients with nontuberculous pleural effusion. Pleural effusion T-SPOT.TB, blood T-SPOT.TB, pleural effusion Xpert MTB/RIF, and pleural effusion adenosine deaminase (ADA) tests were performed before treatment in the included patients. The sensitivity of pleural effusion T-SPOT.TB was higher than that of peripheral blood T-SPOT.TB (76.32%, 58/76), pleural effusion Xpert MTB/RIF (65.79%, 50/76), and pleural effusion ADA (28.95%, 22/76); the differences were statistically significant (x2 = 14.74, 25.22, and 76.45, P < 0.01). The specificity of the Xpert MTB/RIF test for pleural effusion (100%, 36/36) was higher than that for pleural effusion T-SPOT.TB (77.78%, 28/36), peripheral blood T-SPOT.TB, and pleural effusion T-SPOT.TB. The sensitivity of the combined Xpert MTB/RIF test (64.47%, 49/76) was lower than that of the pleural effusion T-SPOT.TB alone (97.37%, 74/76). [ABSTRACT FROM AUTHOR]

This paper puts forward a novel image encryption algorithm that is based on permutation-diffusion architecture. In pixels' permutation stage, algorithm takes full advantage of the idea of magic cube's scrambling. There is only simple cyclic shift operation in each sub-block's permutation, but when the algorithm has disposed the current sub-block, the adjacent sub-blocks will be dealt with, too. In the cyclic shift of each row, stable points will help to decrease the correlation of adjacent pixels. To make encryption procedure uncertain, this paper brings in a parameter named delay-time that is generated by chaotic map. In the diffusion stage, by combining multiple operations and dynamic look-up table together, the proposed algorithm highly increases the uncertainty of the encryption procedure. At last, the experiment results of key space analysis, information entropy analysis, histogram analysis and etc. show that the encryption algorithm has well performance and it can be used in image encryption and transmission. [ABSTRACT FROM AUTHOR]

Gaussian process regression has proven very powerful in statistics, machine learning and inverse problems. A crucial aspect of the success of this methodology, in a wide range of applications to complex and real-world problems, is hierarchical modeling and learning of hyperparameters. The purpose of this paper is to study two paradigms of learning hierarchical parameters: one is from the probabilistic Bayesian perspective, in particular, the empirical Bayes approach that has been largely used in Bayesian statistics; the other is from the deterministic and approximation theoretic view, and in particular the kernel flow algorithm that was proposed recently in the machine learning literature. Analysis of their consistency in the large data limit, as well as explicit identification of their implicit bias in parameter learning, are established in this paper for a Matérn-like model on the torus. A particular technical challenge we overcome is the learning of the regularity parameter in the Matérn-like field, for which consistency results have been very scarce in the spatial statistics literature. Moreover, we conduct extensive numerical experiments beyond the Matérn-like model, comparing the two algorithms further. These experiments demonstrate learning of other hierarchical parameters, such as amplitude and lengthscale; they also illustrate the setting of model misspecification in which the kernel flow approach could show superior performance to the more traditional empirical Bayes approach. [ABSTRACT FROM AUTHOR]

In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues on target parameter estimation. To obtain a high-precision signal recovery, especially in scenarios involving closely spaced targets, the RGDP−SBL algorithm makes use of a three−level hierarchical prior model. Furthermore, the RGDP−SBL algorithm efficiently utilizes diagonal elements during the coarse search and exploits the convexity of the grid energy curve during the fine search, therefore significantly reducing computational complexity. Simulation results demonstrate that the RGDP−SBL algorithm significantly improves signal recovery performance while maintaining low computational complexity in multiple scenarios for RFPA radar. [ABSTRACT FROM AUTHOR]

MCMC algorithm is widely used in parameters' estimation of GARCH-type models. However, the existing algorithms are either not easy to implement or not fast to run. In this paper, Hamiltonian Monte Carlo (HMC) algorithm, which is easy to perform and also efficient to draw samples from posterior distributions, is firstly proposed to estimate for the Gaussian mixed GARCH-type models. And then, based on the estimation of HMC algorithm, the forecasting of volatility prediction is investigated. Through the simulation experiments, the HMC algorithm is more efficient and flexible than the Griddy-Gibbs sampler, and the credibility interval of forecasting for volatility prediction is also more accurate. A real application is given to support the usefulness of the proposed HMC algorithm well. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, we propose a method to classify and detect normal, known anomalies, and unknown anomalies by combining the expectation-maximisation (EM-λ) algorithm and the Mahalanobis-Taguchi (MT) method. Methodology/Approach: The proposed method learns normal data that are expected to be homogeneous and known abnormal data and performs classification and detection by parameter estimation using the EM-λ algorithm. Conventional methods perform analysis based on parameter estimation using the EM algorithm. However, the EM algorithm can degrade classification accuracy if it does not assume that the data fits the model's generative process. Findings: We verify the performance of the proposed method using artificially generated data and real-world bean data for classification as data that do not satisfy this assumption. The validation results show up to 6% improvement over the conventional method in classification accuracy and unknown anomaly discrimination accuracy. Research Limitation/implication: We try various patterns for the parameter of the proposed method in the verification. However, this way is computationally expensive. Originality/Value of paper: Conventional methods perform analysis based on parameter estimation using the EM algorithm. Our proposal method seeks to improve accuracy by using the EM-λ algorithm for parameter estimation, which is expected to improve classification accuracy when the data do not conform to the generative assumptions of the EM algorithm's model. Category: Conceptual paper. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a Reduced-Dimension Multiple-Signal Classification (RD-MUSIC) technique via Higher-Order Orthogonal Iteration (HOOI), which facilitates the estimation of the target range and angle for Frequency-Diverse Array Multiple-Input–Multiple-Output (FDA-MIMO) radars in the unfolded coprime array with unfolded coprime frequency offsets (UCA-UCFO) structure. The received signal undergoes tensor decomposition by the HOOI algorithm to get the core and factor matrices, then the 2D spectral function is built. The Lagrange multiplier method is used to obtain a one-dimensional spectral function, reducing complexity for estimating the direction of arrival (DOA). The vector of the transmitter is obtained by the partial derivatives of the Lagrangian function, and its rotational invariance facilitates target range estimation. The method demonstrates improved operation speed and decreased computational complexity with respect to the classic Higher-Order Singular-Value Decomposition (HOSVD) technique, and its effectiveness and superiority are confirmed by numerical simulations. [ABSTRACT FROM AUTHOR]

This paper deals with parameter estimation problem of large‐scale systems. A recursive distributed parameter estimation algorithm, based on the minimization of the prediction estimation error method, is developed. Specifically, the class of large‐scale systems that are composed of several interconnected sub‐systems is considered. Each interconnected sub‐system is modelled by a linear discrete‐time state space mathematical model with unknown parameters. The convergence analysis is then achieved using the Lyapunov approach. The theoretical analysis and simulation results prove the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an unambiguous two-dimensional (2D) direction of arrival (DOA) estimation algorithm based on a large-space L-shaped array is proposed. The proposed L-shaped array is composed of two large-space linear arrays. Each linear array consists of two uniform linear arrays with internal element spacing being larger than half wavelength of incident signal. Firstly, an unambiguous modified estimation of signal parameter via rotational invariance techniques (ESPRIT) algorithm is proposed to estimate the elevation angles. Then, using the estimated elevation angles, automatically matched azimuth angles can be estimated. On account of the adjustable element spacing, the proposed array is robust to mutual coupling effect. Moreover, simulation results can prove that the proposed algorithm has higher estimation accuracy than many similar 2D DOA estimation algorithms based on L-shaped array. [ABSTRACT FROM AUTHOR]

Fractional‐order calculus has broad application scenarios in engineering and physics. Unlike integer‐order calculus, fractional‐order calculus has the ability to analyze nonclassical phenomena in science and engineering. For industrial processes with strong nonlinear characteristics, nonlinear models such as the Wiener model have become research hotspots. This paper studies the parameter identification of the fractional‐order Wiener system. In this paper, the forgetting factor extended stochastic gradient (FF‐ESG) algorithm and the gradient iterative (GI) algorithm are proposed to identify the parameters of the fractional‐order Wiener system. Then, the convergence of the FF‐ESG algorithm for the fractional‐order Wiener system is analyzed. Both proposed algorithms can obtain exact parameter estimates, which are verified by a numerical example and a case study of a fluid control valve. [ABSTRACT FROM AUTHOR]

Purpose: The Wiener-Hammerstein nonlinear system is made up of two dynamic linear subsystems in series with a static nonlinear subsystem, and it is widely used in electrical, mechanical, aerospace and other fields. This paper considers the parameter estimation of the Wiener-Hammerstein output error moving average (OEMA) system. Design/methodology/approach: The idea of multi-population and parameter self-adaptive identification is introduced, and a multi-population self-adaptive differential evolution (MPSADE) algorithm is proposed. In order to confirm the feasibility of the above method, the differential evolution (DE), the self-adaptive differential evolution (SADE), the MPSADE and the gradient iterative (GI) algorithms are derived to identify the Wiener-Hammerstein OEMA system, respectively. Findings: From the simulation results, the authors find that the estimation errors under the four algorithms stabilize after 120, 30, 20 and 300 iterations, respectively, and the estimation errors of the four algorithms converge to 5.0%, 3.6%, 2.7% and 7.3%, which show that all four algorithms can identify the Wiener-Hammerstein OEMA system. Originality/value: Compared with DE, SADE and GI algorithm, the MPSADE algorithm not only has higher parameter estimation accuracy but also has a faster convergence speed. Finally, the input–output relationship of laser welding system is described and identified by the MPSADE algorithm. The simulation results show that the MPSADE algorithm can effectively identify parameters of the laser welding system. [ABSTRACT FROM AUTHOR]

This paper investigates sparse optimization problems characterized by a sparse group structure, where element- and group-level sparsity are jointly taken into account. This particular optimization model has exhibited notable efficacy in tasks such as feature selection, parameter estimation, and the advancement of model interpretability. Central to our study is the scrutiny of the ℓ 0 and ℓ 2 , 0 norm regularization model, which, in comparison to alternative surrogate formulations, presents formidable computational challenges. We embark on our study by conducting the analysis of the optimality conditions of the sparse group optimization problem, leveraging the notion of a γ -stationary point, whose linkage to local and global minimizer is established. In a subsequent facet of our study, we develop a novel subspace Newton algorithm for sparse group ℓ 0 optimization problem and prove its global convergence property as well as local second-order convergence rate. Experimental results reveal the superlative performance of our algorithm in terms of both precision and computational expediency, thereby outperforming several state-of-the-art solvers. [ABSTRACT FROM AUTHOR]

Joint angle and frequency estimation, one of the key technologies in wireless communication and radar science, has been extensively studied by scholars. For linear arrays, this paper proposes a joint angle and frequency estimation method based on covariance reconstruction and the estimation of signal parameters via rotational invariance techniques (CR-ESPRIT). We first use the received conjugate signal to reconstruct a covariance matrix. Then, we use the least squares-ESPRIT (LS-ESPRIT) algorithm to estimate the desired frequencies. Finally, we estimate the angles according to the reconstructed matrix. The proposed method can estimate signal parameters via automatic pairing and without an additional parameter pairing process under the condition of a uniform or a nonuniform array. Moreover, this method has high estimation accuracy, excellent and stable anti-noise performance, and strong algorithmic robustness. Through a computer simulation analysis, we can confirm the reliability and validity of the proposed parameter estimation method. A comparison with other methods further proves the performance advantages of the developed method. The method in this paper can be easily applied to many signal processing contexts, such as electronic reconnaissance and wireless communication. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

The parametric estimation of stochastic error signals is a common task in many engineering applications, such as inertial sensor calibration. In the latter case, the error signals are often of complex nature, and very few approaches are available to estimate the parameters of these processes. A frequently used approach for this purpose is the maximum likelihood (ML), which is usually implemented through a Kalman filter and found via the expectation-maximization algorithm. Although the ML is a statistically sound and efficient estimator, its numerical instability has brought to the use of alternative methods, the main one being the generalized method of wavelet moments (GMWM). The latter is a straightforward, consistent, and computationally efficient approach, which nevertheless loses statistical efficiency compared with the ML method. To narrow this gap, in this paper, we show that the performance of the GMWM estimator can be enhanced by making use of model moments in addition to those provided by the vector of wavelet variances. The theoretical findings are supported by simulations that highlight how the new estimator not only improves the finite sample performance of the GMWM but also allows it to approach the statistical efficiency of the ML. Finally, a case study with an inertial sensor demonstrates how useful this development is for the purposes of sensor calibration. [ABSTRACT FROM AUTHOR]

The multipath effect error (MEE) is typically not taken into account by the RTKLIB localization method, and this may lead to poor positioning accuracy. This paper proposes an expectation maximization (EM) algorithm for GPS positioning based on Volterra series, and the pseudoranges contaminated by MEE are considered as missing data. Firstly, the Volterra series is introduced to linearize the pseudorange equation. Then, the EM algorithm is used to iteratively update the user location and missing data. Compared with the RTKLIB method, the proposed algorithm has more accurate positioning accuracy. The simulation example shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of identifying a system among a family of given systems. Thus, from measurements collected on an unidentified system but that is part of a family of known model systems, we seek to determine this unidentified system. This differs from identifying the parameters of a given system through experimental observations [15]. The determination (identification) in a given family not always being possible, we refer to the identifiable family as any family for which this identification is possible. We thus introduce the concept of identifiability of a family of systems through a given measurement function. For localized linear systems we give algebraic characterizations that use the notion of system observability. We then propose algorithms which, in case of identifiability of the family and by a process of elimination, identify the system to which the collected measurements correspond. We have given some examples to illustrate these algorithms. We have also added an exemplified extension to discrete localized systems. [ABSTRACT FROM AUTHOR]

The dictionary grid mismatch problem in sparse processing is an important factor affecting parameter reconstruction. The existing grid correction algorithms are limited by the initial grid division and the accurate estimation of the support set vector. Furthermore, these methods fail if multiple scattering points fall into the same grid interval. In this paper, a constrained inhomogeneous grid optimization sparse processing algorithm is proposed. The proposed method includes two sub-processes, namely initial grid optimization and the support set vector correction. The optimization of the initial grid is to use the idea of the coordinate descent method to iteratively generate an updated grid vector, which is used for the inhomogeneous fission of the grid around the target from coarse to dense. Moreover, under the constraint of dictionary atom maximum correlation, multiple scattering points are separated in different grids, which improves the search accuracy of support set atoms. The correction process of the support set vector is to use the first-order Taylor expansion of the dictionary grid to linearly approximate the real parameters of the target, to achieve adaptive correction of dictionary mismatched atoms. According to the simulation experiment of the frequency agile radar scenario, the proposed algorithm can achieve higher range-Doppler parameter joint estimation accuracy in the multi-scattering point scenario in comparison with the conventional sparse recovery algorithm and grid point correction algorithm. [ABSTRACT FROM AUTHOR]

The performance of direction-of-arrival (DOA) estimation algorithms degrades when a partly calibrated array is adopted due to the existing unknown gain-phase uncertainties. In addition, the spatial discretized searching grid also limits the performance improvement and effectiveness of subspacebased DOA estimation algorithms, especially when the true angles do not lie on the grid points which is referred to the off-grid problem alike. In this paper, a self-calibration DOA estimation algorithm is proposed which solves the array calibration and off-grid problems simultaneously. Firstly, the signal model for a partly calibrated array with gain-phase uncertainties is established. To suppress the off- grid errors, an optimization problem for joint parameters estimation is constructed by substituting the approximation of the steering vector into a newly constructed objective function. The alternative minimization (AM) algorithm is employed to calculate the joint DOA and gain-phase uncertainty estimations. Within each iteration step of the optimization problem, a closed-form solution is derived that guarantees the convergence of the proposed algorithm. Furthermore, the Cram´er-Rao bound (CRB) for the partly calibrated arrays with unknown gain-phase uncertainties is also derived and analyzed in the paper. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The stochastic subspace algorithm is one of the most widely used structural identification techniques, which is generally involved with the stabilization diagram for estimating modal parameters. However, the conventional stabilization diagram has an inherent problem: some spurious modes may be identified as stable results, resulting in adverse effects on structural modal identification. To address this critical issue, this paper proposes an improved stochastic subspace algorithm involving a Monte Carlo–based stabilization diagram. Through a numerical simulation study, the good performance of the Monte Carlo–based stabilization diagram for discriminating the poles denoting the physical modes from those representing spurious modes is demonstrated. The numerical simulation results show that the proposed method can estimate structural modal parameters with high accuracy and robustness. Moreover, the proposed method is applied to field measurements on a 600-m-high skyscraper during Super Typhoon Mangkhut, and the results verify the applicability and effectiveness of the proposed method to field measurements. This paper aims to provide an effective tool for accurate estimation of modal parameters of civil structures. [ABSTRACT FROM AUTHOR]

Marine navigation systems have very accurate sensors, such as 0.01deg/hr gyro drift stability and 0.1mg/year accelerometer bias stability. Common calibration methods and equipment do not meet the accuracy required. In this paper, a systematic method for calibration of an inertial measurement unit (IMU) for marine applications is proposed which is not based on the accuracy of the calibration turn table and only requires one specific plate to determine the initial attitude of the IMU and functions independently of the turn table. The first contribution of this paper is to derive a model for systematic calibration of IMU that expresses the rotation matrix error and velocity as a linear function of the calibration parameters at any time. As the second contribution, this paper proposes a calibration algorithm with only using an initial, specific plate. Using the actual data, it was found that the proposed algorithm provides a good estimation of the parameters. [ABSTRACT FROM AUTHOR]

The most common estimation algorithms used today for power system static and dynamic state estimation are the variants of Kalman filter (KF) like Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These model based estimation algorithms are well known for their accuracies. However, it is a well known fact that EKF requires fine tuning and good initial guess for optimum performance. Moreover, these adaptive filters generally employed for estimation purposes require high computational power when it comes to real time estimation. Therefore, in this paper we propose a computationally light yet effective estimation algorithm based on state space model which have not yet been applied to the problem of power system dynamic state estimation. We derive and propose the use of state space least mean fourth algorithm for the purpose of dynamic state estimation considering the problem of a two phase permanent magnet synchronous motor. The algorithm has been employed successfully in this paper in the dynamic state estimation of the highly non linear synchronous motor. The problem has been investigated in the presence of Gaussian noise to show the effectiveness of the algorithm. Moreover, the algorithm is also compared with the performance of the EKF. [ABSTRACT FROM AUTHOR]

Estimation of induction machine parameters, which are commonly used in efficiency evaluation and control methods, can be effectively achieved by utilizing genetic algorithms (GAs). One of the difficulties of using GAs for efficiency estimation is the determination of variable (parameter) constraints (ranges). This paper focuses on the range determination of the parameters (variables) for GA applications. A wide range of variables can cause unstable outcomes. Hence, it is essential to determine an acceptable range for each variable prior to a GA run to produce stable and repeatable results. In this paper, relationships based on the nameplate information and a large database of tested induction motors provided by Hydro-Québec are proposed to determine reasonable induction motor parameter ranges. The proposed method is applied to three different cases. The results of the three cases are compared to each other and also against the corresponding experimental results, which validate the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses on multi-target parameter estimation of multiple-input multiple-output (MIMO) radar with widely separated antennas on moving platforms. Aiming at the superimposed signals caused by multi-targets, the well-known expectation maximization (EM) is used in this paper. Target's radar cross-section (RCS) spatial variations, different path losses and spatially-non-white noise appear because of the widely separated antennas. These variables are collectively referred to as signal-to-noise ratio (SNR) fluctuations. To estimate the echo delay/Doppler shift and SNR, the Q function of EM algorithm is extended. In addition, to reduce the computational complexity of EM algorithm, the gradient descent is used in M-step of EM algorithm. The modified EM algorithm is called generalized adaptive EM (GAEM) algorithm. Then, a weighted iterative least squares (WILS) algorithm is used to jointly estimate the target positions and velocities based on the results of GAEM algorithm. This paper also derives the Cramér-Rao bound (CRB) in such a non-ideal environment. Finally, extensive numerical simulations are carried out to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Transmission line is a vital part of the power system that connects two major points, the generation, and the distribution. For an efficient design, stable control, and steady operation of the power system, adequate knowledge of the transmission line parameters resistance, inductance, capacitance, and conductance is of great importance. These parameters are essential for transmission network expansion planning in which a new parallel line is needed to be installed due to increased load demand or the overhead line is replaced with an underground cable. This paper presents a method to optimally estimate the parameters using the input-output quantities i.e., voltages, currents, and power factor of the transmission line. The equivalent π-network model is used and the terminal data i.e., sending-end and receiving-end quantities are assumed as available measured data. The parameter estimation problem is converted to an optimization problem by formulating an error-minimizing objective function. An improved particle swarm optimization (PSO) in terms of time-varying control parameters and chaos-based initialization is used to optimally estimate the line parameters. Two cases are considered for parameter estimation, the first case is when the line conductance is neglected and in the second case, the conductance is considered into account. The results obtained by the improved algorithm are compared with the standard version of the algorithm, firefly algorithm and artificial bee colony algorithm for 30 number of trials. It is concluded that the improved algorithm is tremendously sufficient in estimating the line parameters in both cases validated by low error values and statistical analysis, comparatively. [ABSTRACT FROM AUTHOR]

In this paper, we review the derivation of the Gauss–Levenberg–Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non‐unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we use the maximum likelihood principle and the negative gradient search principle to study the identification issues of the multivariate equation‐error systems whose outputs are contaminated by an moving average noise process. The model decomposition technique is used to decompose the system into several regressive identification subsystems based on the number of the outputs. In order to improve the parameter estimation accuracy, a decomposition‐based multivariate maximum likelihood gradient iterative algorithm is proposed by means of the maximum likelihood principle and the iterative identification method. The numerical simulation example indicates that the proposed method has better parameter estimation results than the compared decomposition‐based multivariate maximum likelihood gradient algorithm. [ABSTRACT FROM AUTHOR]

The controlled autoregressive autoregressive moving average (CARARMA) models are of popularity to describe the evolution characteristics of dynamical systems. To overcome the identification obstacle resulting from colored noises, this paper studies the identification of the CARARMA models by forming an intermediate correlated noise model. In order to realize the real-time prediction function of the models, the on-line identification scheme is developed by constructing the dynamical objective functions based on the real-time sampled observations. Firstly, a rolling optimization cost function is built based on the observation at a single sampling instant to catch the modal information at a single time point and a generalized extended stochastic gradient (GESG) algorithm is proposed through the stochastic gradient optimization. Secondly, a rolling window cost function is built in accordance with the dynamical batch observations within data window by extending the proposed GESG algorithm and the multi-innovation generalized extended stochastic gradient algorithm is derived. Thirdly, from the perspective of theoretical analysis, the convergence proof of the proposed algorithm is provided based on the stochastic martingale convergence theory. Finally, the simulation analysis and comparison studies are provided to show the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel normalized least mean squares (NLMS) algorithm for system identification applications. Our approach involves analyzing the mean squared deviation performance of the NLMS algorithm using a random walk model to select two optimal parameters, the step size and regularization parameters, for the rapid convergence of the colored input signals. We verified that the proposed algorithm exhibited faster convergence than existing algorithms, even in scenarios of sudden system changes. [ABSTRACT FROM AUTHOR]

In the complex multipath propagation environment, whether there is a line of sight (LOS) path will directly affect the positioning accuracy. Therefore, this paper proposes a single base station hybrid positioning algorithm based on LOS identification. In the algorithm, we first construct multiple features based on channel state information with LOS and without LOS in statistical distribution and use these features and gradient boosting decision tree to determine whether there is a LOS path in the environment. Then for the environment with a LOS path, we proposed a positioning method based on the estimation of signal parameters via rotation invariant technology algorithm which can be used to jointly estimate the angle of arrival and time delay of the LOS path for positioning, while for the environment without LOS path, we propose a positioning method based on adaptive genetic algorithm. Finally, a single base station hybrid positioning algorithm based on LOS identification and the corresponding positioning methods. Simulation results show that the proposed hybrid positioning algorithm can achieve high-precision positioning in the complex multipath propagation environment. [ABSTRACT FROM AUTHOR]

Orthogonal time frequency space (OTFS) is a new method technique that supports reliable information transmission in a strong Doppler environment. Aiming at the radar target parameter estimation problem of integration of radar and communication (IRC) based on OTFS, this paper proposes a two‐step radar target parameter estimation algorithm combining maximum likelihood (ML) estimation and gradient descent (GD) principle. Firstly, the echo signal of the OTFS integrated signal is constructed and based on this, the ML estimation model is constructed, and the alternating projection (AP) algorithm is employed to make a rough estimation of the target parameters. Then the rough estimation results of the AP‐ML algorithm are employed as the initial value of the GD algorithm for the fine parameter estimation. Simulation results show that the proposed algorithm has better target parameter estimation performance and resolution while reducing the computational complexity. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect. [ABSTRACT FROM AUTHOR]