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In this paper, we analyze the generalization performance of the Iterative Hard Thresholding (IHT) algorithm widely used for sparse recovery problems. The parameter estimation and sparsity recovery consistency of IHT has long been known in compressed sensing. From the perspective of statistical learning, another fundamental question is how well the IHT estimation would predict on unseen data. This paper makes progress towards answering this open question by introducing a novel sparse generalization theory for IHT under the notion of algorithmic stability. Our theory reveals that: 1) under natural conditions on the empirical risk function over $n$ samples of dimension $p$ , IHT with sparsity level $k$ enjoys an $\tilde {\mathcal {O}}(n^{-1/2}\sqrt {k\log (n)\log (p)})$ rate of convergence in sparse excess risk; 2) a tighter $\tilde {\mathcal {O}}(n^{-1/2}\sqrt {\log (n)})$ bound can be established by imposing an additional iteration stability condition on a hypothetical IHT procedure invoked to the population risk; and 3) a fast rate of order $\tilde {\mathcal {O}}\left ({n^{-1}k(\log ^{3}(n)+\log (p))}\right)$ can be derived for strongly convex risk function under proper strong-signal conditions. The results have been substantialized to sparse linear regression and sparse logistic regression models to demonstrate the applicability of our theory. Preliminary numerical evidence is provided to support our theoretical predictions. [ABSTRACT FROM AUTHOR]

A good quality stability model is a key factor for accurate power system operations.Inaccurate parameters of the stability models affect the decision making which paves the way for serious consequences. Thus, it is necessary to calibrate the stability model parameters in a regular manner. There are several calibration methods in the literature which are based on simultaneous estimation of the parameters and states. However, not all of the model parameters are well estimable simultaneously. Simultaneous estimation of parameters with high collinearity may result in biased calibration results. In this paper, the trajectory sensitivity method is used to detect the sensitive parameters and construct the sensitivity matrix. Then, parameters with high linear dependency are identified using the sensitivity matrix. It is shown that, despite the high sensitivity of a parameter, its estimability degrades as the collinearity with other parameters increase. In this paper an identifiability analysis that detects the collinearity among the sensitive parameters is proposed. The proposed method is validated using WSCC 9-Bus System. [ABSTRACT FROM AUTHOR]

This paper concerns the detection and identification of errors in the parameters of three-phase transposed as well as untransposed transmission line (TL) models used by various network applications. Such detailed models are increasingly needed in particular by power system applications where loads may not be balanced and/or TLs may not be symmetrical, and a full detailed three-phase solution may have to be obtained. To address this problem, the paper proposes an efficient algorithm for detection, identification, and estimation of parameter errors using synchronized phasor measurements. The suspect TL is detected using the modal domain networks in the first stage. The developed algorithm extends the previously developed largest normalized Lagrange multiplier (NLM) test for positive sequence parameters to the full coupled three-phase lines. An estimation method is also proposed for estimating the erroneous parameters, which takes into account the correlation of the parameters. To illustrate the effectiveness of the method, several tests are performed on the IEEE 118-bus system and a large 3474-bus utility system. [ABSTRACT FROM AUTHOR]

Translation averaging is known to be more difficult than rotation averaging due to scale ambiguity, estimation sensitivity, and solution uncertainty. Existing approaches have exposed their limitations in terms of accuracy, robustness, simplicity, or efficiency. To tackle this tough problem, a simple yet effective translation averaging pipeline, termed as Incremental Translation Averaging (ITA), is proposed in this paper. It combines the advantages of high accuracy and robustness in incremental parameter estimation pipeline and the advantages of high simplicity and efficiency in global motion averaging approach. Unlike the traditional translation averaging methods which estimate all the absolute camera locations simultaneously and suffer from inaccuracy in parameter estimation and incompleteness in scene reconstruction, our ITA computes them novelly in an incremental way with higher accuracy and robustness. Thanks to the introduction of incremental parameter estimation thought into the translation averaging pipeline, 1) our ITA is robust to measurement outliers and accurate in parameter estimation; and 2) our ITA is simple and efficient because of its less dependency on complicated optimization, carefully-designed preprocessing, or additional information. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of our ITA and its advantages over several state-of-the-art translation averaging approaches. [ABSTRACT FROM AUTHOR]

This paper presents an alternative adaptive parameter estimation framework for nonlinear systems with time-varying parameters. Unlike existing techniques that rely on the polynomial approximation of time-varying parameters, the proposed method can directly estimate the unknown time-varying parameters. Moreover, this paper proposes several new adaptive laws driven by the derived information of parameter estimation errors, which achieve faster convergence rate than conventional gradient descent algorithms. In particular, the exponential error convergence can be rigorously proved under the well-recognized persistent excitation condition. The robustness of the developed adaptive estimation schemes against bounded disturbances is also studied. Comparative simulation results reveal that the proposed approaches can achieve better estimation performance than several other estimation algorithms. Finally, the proposed parameter estimation methods are verified by conducting experiments based on a roto-magnet plant. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array structure and coprime frequency offsets, a two-dimensional space-frequency virtual difference coarray corresponding to uniform array and uniform frequency offset is considered to increase the number of degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz covariance matrix is computationally prohibitive. To solve this problem, we propose an interpolation algorithm based on decoupled atomic norm minimization (DANM), which converts the coarray signal to a simple matrix form. On this basis, a relaxation-based optimization problem is formulated to achieve joint DoA-range estimation with enhanced DoFs. The reconstructed coarray signal enables application of existing subspace-based spectral estimation methods. The proposed DANM problem is further reformulated as an equivalent rank-minimization problem which is solved by cyclic rank minimization. This approach avoids the approximation errors introduced in nuclear norm-based approach, thereby achieving superior root-mean-square error which is closer to the Cramér-Rao bound. The effectiveness of the proposed method is confirmed by theoretical analyses and numerical simulations. [ABSTRACT FROM AUTHOR]

In order to improve the trajectory tracking accuracy and vehicle lateral stability, the paper proposes a lateral and longitudinal dynamics control framework of autonomous vehicles considering multi-parameter joint estimation. First, the multi-parameter joint estimation based on adaptive unscented Kalman filter (AUKF) is constructed to decouple and estimate the longitudinal position of vehicle's center of gravity (CG), tire-road friction coefficient (TRFC), tire cornering stiffness (TCS), tire vertical force (TVF) and road grade. Then, focusing on the large lateral acceleration condition, a lateral control based on the optimal front-tire lateral force is proposed by constructing the linear quadratic regulator (LQR) and combining the multi-parameter joint estimation. Additionally, a longitudinal control based on the drive and brake force compensation to realize the accurate speed tracking by combining road slop estimation is achieved. The fuzzy system employs the drive and brake force deviation as the input to implement the compensation of the throttle and brake. The proposed estimation and control framework are verified by co-simulation on PreScan and CarSim preliminarily. In order to further verify the effectiveness and practicability of algorithm, experiments are implemented on an autonomous vehicle platform, a hybrid Lincoln MKZ. Simulation and experimental results show that the proposed estimation and control framework possess excellent performance and enhance the tracking accuracy and lateral stability. [ABSTRACT FROM AUTHOR]

Dual three-phase permanent magnet synchronous motors (DTP-PMSMs) feature a satisfactory capability of the fault-tolerant operation due to the inherent multiphase configuration. To improve the control performance of the DTP-PMSMs under the open-phase fault, accurate inductances are critical. This paper presents an extended Kalman Filter (EKF) based inductance estimation strategy for DTP-PMSMs with one phase opened. Firstly, the multi-synchronous-rotating frame (MSRF) based DTP-PMSMs modeling under the single open-phase fault is presented. Secondly, the system space-state model and proposed EKF based estimation algorithm considering inverter nonlinearity are presented. Finally, the inductance estimation is performed by experiments under both steady and transient states. The estimation results have been verified by torque measurements which confirm the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Massive multiple-input-multiple-output (MIMO) and millimeter wave have been adopted as the enabling technologies for the 5G and beyond 5G (B5G) systems. A challenging problem introduced by the use of large antenna size and wide bandwidth is beam squint, i.e., spatial-wideband effect. Beam squint can significantly degrade the channel estimation performance for conventional channel estimators. Research effort on channel estimation under beam squint conditions has been very limited. For the few available work that attempts to address this problem, they require either all subcarriers or multiple symbols used as pilot for channel estimation, so large overhead becomes inevitable. Therefore, in this paper, we propose an efficient channel estimation method that only requires a small number of subcarriers. The channel estimation problem is formulated as a nonlinear least squares optimization problem. Initial parameter estimation is critical, which will affect the efficiency and convergence of the proposed algorithm. Using a densely-spaced antenna structure and consecutive subcarriers assignment approach, we can effectively avoid the aliasing effect and reduce the ambiguity during the initialization phase. A subcarrier assignment criterion is proposed to achieve the optimal performance. Closed-form expressions of the Cramér-Rao lower bound (CRLB) and the achievable rate are derived to evaluate the performance. Both simulation results and theoretical analysis show that even with a small number of subcarriers, the estimation error closely approaches the CRLB, and its effect is negligible compared with the noise when evaluating the signal-to-noise ratio with a simple linear detector. Furthermore, the number of pilot subcarriers has little impact on the achievable rate. [ABSTRACT FROM AUTHOR]

The occurrence of motion blur along with defocus blur is a common phenomena in natural images. Usually, these blurs are spatially varying in nature for any general image and estimation of one type of blur is affected by presence of other. In this paper, we propose a novel method to estimate the concurrent defocus and motion blurs in a single image. Unlike the recent methods, which perform well only on simulated conditions or in presence of single type of blur, proposed method works well for real images as well as for compressed images. In this paper, we consider only commonly associated motion and defocus blurs for analysis. Decoupling of motion and defocus blur provides a fundamental tool that can be used for various analysis and applications. [ABSTRACT FROM AUTHOR]

This paper considers the problem of secure parameter estimation when an estimation algorithm is prone to causative attacks. Causative attacks, in general, target decision-making algorithms (e.g., inference or learning algorithm) to alter their decisions in specific scenarios (e.g., distort parameter estimates for specific ranges of the parameter of interest). Such attacks influence the decisions via tampering with the mechanisms through which an algorithm acquires the statistical model of the population about which it aims to form a decision. Such attacks are viable, for instance, by contaminating the historical or training data, or by compromising an expert who provides the statistical model. In the presence of causative attacks, inference algorithms operate under a distorted statistical model for the data samples. This paper introduces a notion of secure parameter estimation and formalizes a framework under which secure estimation can be formulated and analyzed. The central premise underlying the secure estimation framework is that forming secure estimates introduces a new dimension to the estimation objective, pertaining to detecting attacks and isolating the true model. Since detection and isolation decisions themselves are imperfect, their inclusion induces an inherent coupling between the desired secure estimation objective and the auxiliary detection and isolation decisions that need to be formed in conjunction with the estimates. This paper establishes the fundamental interplay among these decisions, and characterizes the general decision rules in closed-forms for any desired estimation cost function. Furthermore, to circumvent the computational complexity associated with growing parameter dimension or attack complexity, a scalable estimation algorithm is provided, which is shown to enjoy certain optimality guarantees. Finally, the theory developed is applied to secure parameter estimation in sensor networks. [ABSTRACT FROM AUTHOR]

Robust spectral analysis of the sinusoidal signals corrupted by impulsive noise poses a big challenge in the signal processing community. In this paper, we address the issue of robust spectral analysis for multi-channel sinusoidal signals, including order detection and parameter estimation. The successive robust low-rank decomposition is firstly designed to extract the common signal subspace from the multi-channel data matrix. Subsequently, the number of sinusoidal poles is determined with a model order selection criterion, based on the so-obtained subspace. With the signal order information, the sinusoidal parameters and outliers are jointly estimated according to the maximum a posteriori criterion. To find a robust initial guess of the sinusoidal parameters, an estimator based on robust weighted linear prediction is developed. Additionally, the performance analysis is provided, which includes computational complexity, convergence verification of the sinusoidal parameter estimation, and asymptotic consistency of the signal order detection. Simulation results demonstrate the advantages of the proposed robust spectral analysis framework compared to state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Most works in the literature on parameter estimation with multiple-input multiple-output (MIMO) systems are limited to estimating only the direction of arrival (DOA), and/or based on the far-field assumption. However, the joint estimation of DOA and range is an important challenge for near-field (NF) applications. In addition, backscatter coefficient estimation, as one of the most important parameters of radar systems, has been neglected in existing works. In this paper, the joint estimation of the DOA, range and reflectivity is presented for a MIMO array in the NF. By employing the symmetry property in the array, first, two special spatial expressions based on fourth-order and second-order statistics from the received data are defined so that the output of the first expression lacks targets’ range information. By collecting the information of all the sensors and calculating all the interactions between the transmit and receive sensors based on the first expression, a spatial cumulant matrix is constructed. In two different approaches, with/without the use of eigendecomposition, the virtual steering vectors are extracted. Next, DOAs are estimated by a one-dimensional (1D) spectral search. The second expression, which contains both angle and range information, is used to construct several covariance submatrices and then to construct a covariance matrix with a size corresponding to the array’s dimensions. Then, corresponding ranges are estimated by employing estimated DOAs, eigendecomposition and 1D spectral searches. Finally, the equations required to estimate the reflectivity are derived. The simulation results show the remarkable performance of the proposed method in terms of computational time and estimation accuracy. [ABSTRACT FROM AUTHOR]

The Cox proportional hazards model is a popular semi-parametric model for survival analysis. In this paper, we aim at developing a federated algorithm for the Cox proportional hazards model over vertically partitioned data (i.e., data from the same patient are stored at different institutions). We propose a novel algorithm, namely VERTICOX, to obtain the global model parameters in a distributed fashion based on the Alternating Direction Method of Multipliers (ADMM) framework. The proposed model computes intermediary statistics and exchanges them to calculate the global model without collecting individual patient-level data. We demonstrate that our algorithm achieves equivalent accuracy for the estimation of model parameters and statistics to that of its centralized realization. The proposed algorithm converges linearly under the ADMM framework. Its computational complexity and communication costs are polynomially and linearly associated with the number of subjects, respectively. Experimental results show that VERTICOX can achieve accurate model parameter estimation to support federated survival analysis over vertically distributed data by saving bandwidth and avoiding exchange of information about individual patients. The source code for VERTICOX is available at: https://github.com/daiwenrui/VERTICOX. [ABSTRACT FROM AUTHOR]

Reliability of any electrical system heavily depends on the accuracy of the model used for representing the integrated device components. The design of renewable power sources and energy management systems using ultracapacitor demands adoption of precise mathematical representation of ultracapacitor dynamics. An appropriate model of ultracapacitor not only allows an accurate assessment of its charge/discharge characteristics but also provides a precise estimation of energy storage and power delivery capabilities. However, due to the lack in the usability of data/information provided by the manufacturer, conventional calculus-based ultracapacitor modeling and energy estimation approach often leads to its inaccurate and inefficient utility. In this regard, this paper has focused on fractional calculus-based analytical formulation for power, energy, and efficiency measurement of ultracapacitor and has deployed an upgraded model of the ultracapacitor, which in turn provides a better estimation of its energy efficiency. The work presented in this paper has been supported by extensive experimentation with the ultracapacitor under different input excitations, considering the dependence of energy storage (state of energy) and power delivery capacity of ultracapacitor on the charge/discharge profile. Furthermore, the hybrid optimization-based analysis recommends the requirement and challenges for the design and development of optimal charging/discharging of ultracapacitors for achieving higher energy efficiency. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to extend the semiparametric inference methodology, recently investigated for Real Elliptically Symmetric (RES) distributions, to Complex Elliptically Symmetric (CES) distributions. The generalization to the complex field is of fundamental importance in all practical applications that exploit the complex representation of the acquired data. Moreover, the CES distributions has been widely recognized as a valuable and general model to statistically describe the non-Gaussian behaviour of datasets originated from a wide variety of physical measurement processes. The paper is divided in two parts. In the first part, a closed form expression of the constrained semiparametric Cramér-Rao Bound (CSCRB) for the joint estimation of complex mean vector and complex scatter matrix of a set of CES-distributed random vectors is obtained by exploiting the so-called Wirtinger or $\mathbb {C}\mathbb {R}$ -calculus. The second part deals with the derivation of the semiparametric version of the Slepian-Bangs formula in the context of the CES model. Specifically, the proposed semiparametric Slepian-Bangs (SSB) formula provides us with a useful and ready-to-use expression of the semiparametric Fisher Information Matrix (SFIM) for the estimation of a parameter vector parametrizing the complex mean and the complex scatter matrix of a CES-distributed vector in the presence of unknown, nuisance, density generator. Furthermore, we show how to exploit the derived SSB formula to obtain the semiparametric counterpart of the Stochastic CRB for Direction of Arrival (DOA) estimation under a random signal model assumption. Simulation results are also provided to clarify the theoretical findings and to demonstrate their usefulness in common array processing applications. [ABSTRACT FROM AUTHOR]

This paper presents a practical procedure regarding inertia estimation and speed loop auto-tuning for sensorless interior permanent-magnet synchronous motor (IPMSM) drives. In many applications, well-tuned speed controller is essential to enable dynamic and stable drive operations. However, high-performance speed controller design requires system parameters such as inertia which are unknown at the beginning of drive commissioning. Manual trial-and-error tuning is unattractive due to its blind, lengthy, non-optimal, and potentially unsafe nature. On the other hand, model-based tuning through inertia estimation has unique challenges at this step of self-commissioning. Determining the test torque for a safe and reasonably short test in inertia estimation is a real practical problem because the total inertia can be in wide range in different mechanical systems. Furthermore, feedback controllers are not usable to automatically determine the test torque due to unknown motor parameters. As a solution, this paper proposes an automated test torque selection procedure. Then it identifies the inertia and lastly tunes the speed controller using the identified parameter. Overall, a fully automated speed-loop auto-tuning method is proposed which aims minimal operator involvement. The findings are experimentally verified on an IPMSM drive. In addition, the proposed method can easily be extended to different motor types and sensored or sensorless vector control strategies. [ABSTRACT FROM AUTHOR]

Three-phase grid-connected inverter modeling depends on the equivalent resistance and inductance between the inverter and the grid. However, these parameters are not fixed during the operation of the inverter and vary with the operating conditions. In this paper, a new globally stable adaptive controller is proposed to estimate these values online and update the controller during its operation. A model reference adaptive system (MRAS) based on two fictitious quantities with no physical significance is utilized, namely $M = v \cdot i^*$ and $N = v^* \times i$ , where $v$ and $i$ are the steady-state values of voltage and current in synchronously rotating frame of reference. Detailed stability analysis of the proposed estimation technique has been presented using a Lyapunov energy function-based approach proving the global stability of the technique. Small-signal modeling and analysis of the proposed scheme has also been presented so as to understand the local dynamics of the system. The guidelines to select the parameter of the proposed MRAS system along with updating the controller are elaborated in this paper. Detailed simulation based on MATLAB/Simulink and PLECS, along with experimental studies through a Texas Instruments TMS320F28377S microcontroller, validates the feasibility of the proposed control architecture. [ABSTRACT FROM AUTHOR]

Using two electrically long dipoles to estimate the polarizations of impinging sources, this paper pioneers signal-processing algorithms in closed forms. The existing literature on crossed-dipoles polarimetry are restricted to electrically short dipoles with an electrical length of $(L/\lambda) \le (1/10)$ , which have subpar efficiency in radiation and in reception. In contrast, this paper allows electrically long dipoles—two of them here are perpendicularly oriented but may be collocated or may be separated by a known displacement. Using such a pair of electrically long dipoles for polarization estimation, this paper proposes new closed-form formulas and derives the associated Cramér–Rao bounds. [ABSTRACT FROM AUTHOR]

For the purpose of locating the orthogonal frequency division multiplexing (OFDM) signal emitter efficiently and accurately in multipath environment, an innovative direction position determination (DPD) algorithm based on the multiple carrier characteristic of OFDM signals is proposed in this paper. The time of the arrival (TOA), angle of arrival (AOA) and Doppler frequency shift information contained in the observed data are jointly employed to locate the emitter. High-dimensional search can be avoided by decoupling unknown parameters and the estimation performance is improved due to the expansion of the subspace. The compact closed-form expressions of the Cramer-Rao lower bound (CRLB) corresponding to the parameters of interest are also derived. Simulation results show that compared with the traditional DPD based on MUSIC algorithm, the localization performance of the proposed algorithm can attain the CRLB at higher noise levels. [ABSTRACT FROM AUTHOR]

This paper presents an estimation approach within the framework of uplink massive machine-type-communications (mMTC) that considers the energy limitations of the devices. We focus on a scenario where a group of sensors observe a set of parameters and send the measured information to a collector node (CN). The CN is responsible for estimating the original observations, which are spatially correlated and corrupted by measurement and quantization noise. Given the use of Gaussian sources, the minimum mean squared error (MSE) estimation is employed and, when considering temporal evolution, the use of Kalman filters is studied. Based on that, we propose a device selection strategy to reduce the number of active sensors and a quantization scheme with adjustable number of bits to minimize the overall payload. The set of selected sensors and quantization levels are, thus, designed to minimize the MSE. For a more realistic analysis, communication errors are also included by averaging the MSE over the error decoding probabilities. We evaluate the performance of our strategy in a practical mMTC system with synthetic and real databases. Simulation results show that the optimization of the payload and the set of active devices can reduce the power consumption without compromising the estimation accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to estimate the electrical parameters of five-phase induction motors based on the response to a step voltage applied to the stator with the rotor at standstill. The first part of the method consists of the identification of the parameters of the dynamic model expressed in terms of transfer functions including the fundamental and third-harmonic components of the airgap induction. The second part consists of an open-phase test proposed to estimate the stator leakage inductances of each circuit separately. Further, the impact of iron saturation on the parameters is discussed through Finite Element Analysis of a case study. The accuracy of the proposed method is experimentally assessed through the analysis of the behavior of a 4.5-kW motor running in a closed loop under Rotor Field-Oriented Control. The theoretical and practical results suggest that the method can also be applied to self-commissioned drives, given that the estimation proceeds under null torque and speed. The prominent advantages of the proposed method are (i) the possibility of tracking the saturation effects on the parameters, (ii) the tests are easy to execute, and (iii) few sensors are required. [ABSTRACT FROM AUTHOR]

Designing an accurate model for a proton exchange membrane fuel cell (PEMFC) is very difficult owing to its multivariate nature. Hence, PEMFC online system identification (OSI), which serves as a basis for its application in energy management of hybrid fuel cell vehicles, is considerably important to cope with the performance drifts. In this paper, an OSI method is proposed for estimating the time-varying parameters of a well-known PEMFC semi-empirical model in the literature. Unlike the other similar approaches, the proposed technique in this manuscript suggests a Lyapunov-based adaptation law with guaranteed stability to estimate online the fuel cell parameters. To highlight the effectiveness of the suggested approach, it is used to estimate the characteristics of a 500-W Horizon PEMFC and its performance is compared with Kalman filter which is perceived as a reliable linear estimator. Experimental results along with the comparative study prove the successful performance of the suggested technique. [ABSTRACT FROM AUTHOR]

The high precision positioning of global navigation satellite systems (GNSS), which essentially corresponds to solving the integer least-squares (ILS) problem in the integer phase ambiguity estimation, has emerged as a key problem for the development of industrial internet-of-things (IIoT). In this paper, a novel paradigm for solving the ILS problem is introduced to the integer phase ambiguity estimation. Different from the traditional paradigm of ILS which only involves one parameter to characterize the trade-off between performance and complexity, the proposed ILS paradigm entails two parameters named as the initial searching size $K\geq 1$ and the standard deviation $\sigma >0$ , thus introducing extra degrees of freedom to facilitate the system trade-off. Based on it, explicit analysis can be carried out for a mathematically tractable trade-off, where great potentials could be further exploited for the high precision positioning of GNSS. The equivalent searching algorithm (ESA) is proposed, which achieves the same performance as the classic Fincke-Pohst strategy in sphere decoding (SD) but with tractable complexity measured by the number of visited nodes in the searching stage. Moreover, the candidate protection mechanism is given to further upgrade the equivalent searching algorithm, which makes it not only an optimal but also a sub-optimal ILS estimator given the flexible setup of $K$. [ABSTRACT FROM AUTHOR]

In coherent radar system, maneuvering target with complex motions usually confronts the issues of range cell migration (RCM) and Doppler frequency spread (DFS) within the long-time coherent integration process. To address these issues and achieve reliable detection and estimation performance, this paper proposes a novel fast approach without motion parameters searching. Particularly, the proposed approach performs the third-order keystone transform (TKT) to eliminate the third-order RCM (TRCM). Then, the bilinear parametric symmetric self-correlation function (BPSSF), scaled Fourier transform (SCFT) and inverse Fourier transform (IFT) are successively operated to estimate target's velocity and acceleration. Afterwards, the improved second-order phase differentiation (ISPD) is performed to estimate target's jerk. Utilizing the estimated parameters, the compensation functions could be constructed to eliminate the remaining RCMs and DFSs as well as to obtain the energy coherent integration. By contrast, analyses of several classical approaches and the proposed one are provided concerning computational complexity, coherent integration performance, detection performance and estimation performance, which confirm that the proposed approach can balance the computational burden and the detection/estimation capabilities. Additionally, the proposed approach can avoid the effect of blind speed side-lobe (BSSL). Eventually, simulated data verification and raw data processing are conducted to evaluate the efficacy of the presented approach. [ABSTRACT FROM AUTHOR]

This paper deals with the estimation of parameters of a Thevenin equivalent in an AC grid. We consider the practically relevant case of measurements coming from a grid-connected inverter. Our focus is on non-intrusive measurements, as we postulate and later experimentally observe transients that occur in normal operation as the primary source of information. We notice that even during quiet, after work hours there are typically enough variations in the grid to enable successful convergence of our procedure. For completeness, we also consider the artificial case of no external variations and show that injection of very small current perturbations suffices for convergence. Our procedure is based on real-time monitoring of the estimated determinant of the regression matrix. Regression matrix properties are also used to evaluate the integrity of the estimates and to initiate a brief sequence of perturbation in rare cases of the exceptionally restful grid. The estimation algorithm is simple enough to be implementable on standard industrial controllers, yet robust and reliable in terms of speed of convergence. [ABSTRACT FROM AUTHOR]

In this paper, the convolution neural networks (CNN) are developed for the classification and localization of mixed near-field and far-field sources by using the geometry of symmetric nested array. We first transform the received data into frequency domain. Then, we preprocess the phase difference matrix to decouple mixed sources. Considering that the counter-diagonal elements in the phase difference matrix only contain the direction of arrival (DOA) parameter of each mixed source, we utilize the upper right elements as the input of CNN to estimate the DOA of each mixed source. In order to avoid the influence of noise, we construct a particular range vector without the estimated DOA and employ the output of autoencoder to classify the mixed sources. Finally, we further exploit the output of autoencoder and apply the CNN without bias vectors to estimate the range of near-field sources. In contrast to the traditional learning-based approaches regarding the parameter estimation as a classification problem, the proposed approach considers the parameter estimation as a regression problem and can significantly reduce the complexity of networks. Simulation results demonstrate that the proposed learning-based method can improve the precision for the mixed source localization. Moreover, the proposed method is robust to the off-grid parameters and the different numbers of mixed sources. [ABSTRACT FROM AUTHOR]

Recently, maximum correntropy criterion (MCC) has been widely and successfully used in robust signal processing and machine learning, in which the correntropy is maximized instead of minimizing the popular mean square error (MSE) to improve the robustness with respect to outliers or impulsive noises. A lot of efforts have been devoted to derive different adaptive algorithms under MCC, but to date, little insight has been gained as to how the MCC solution will be influenced by outliers. In this paper, we investigate this problem and our focus is mainly on the parameter estimation of a simple linear errors-in-variables (EIVs) model with scalar variables. Under some conditions, we derive an upper bound on the absolute value of the estimation error and show that the MCC solution can get very close to the true value of the unknown parameter even with arbitrarily large outliers in both the input and output variables. Illustrative examples are provided to verify and clarify the theory. [ABSTRACT FROM AUTHOR]

This paper introduces a localization protocol that fills a gap among already existing methods, such as ground penetrating radar or methods based on inductive prospecting. The principle is to attach a radio frequency identification (RFID) tag to a non-metallic object of interest buried in a non-magnetic soil and deduce its depth from the magnetic induction in the near field regime between the buried tag and an RFID antenna. Electromagnetic propagation in soils has been extensively studied in geophysics. The higher the frequency, the lower the penetration depth. The standard frequency range in geophysics is lower than 100 kHz to reach penetration depth as high as several tens of meters and the soil conductance is the main influencing factor. When working with passive RFID tags, their received power and, consequently, the detection range in air grows with the frequency. A frequency tradeoff for the detection in media lies in the range of 10–50 MHz, where the authorized RFID frequency is 13.56 MHZ. In this range, the wavenumber in weakly saline soils depends essentially on the medium dielectric permittivity (see). In this paper, we calculate the precision of the estimation of the burying depth of the tag in terms of the standard deviation and the corresponding bias up to two uncertain parameters—the penetration depth and the activation field. The laboratory experiments provide results supporting the chosen model. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for treating the harmonic torque in permanent magnet motors at selected harmonic orders. The method is based on the online estimation of the motor harmonic flux. For this purpose, a novel minimal parameter harmonic flux estimator (MPHFE) is developed. The MPHFE is formulated such that the inductance, resistance, and stator current and its derivative are not necessary for the estimation of the harmonic flux. This was achieved by forcing the harmonic current to zero through the combined action of a field-oriented controller (FOC) and a feedforward controller. Subsequently, the harmonic flux can be obtained directly from the estimated harmonic back EMF without the involvement of other motor parameters. Finally, the estimated flux is used in conjunction with a comprehensive model of the motor harmonic torque to determine the stator current compensation for eliminating the torque harmonic. Experimental results presented from a permanent magnet motor confirm the theoretical claims in this paper as well as demonstrate the effectiveness of the proposed method to reduce the magnitude of the harmonic torque significantly. [ABSTRACT FROM AUTHOR]

This paper has a twofold goal. The first aim is to provide a deeper understanding of the family of the real elliptically symmetric (RES) distributions by investigating their intrinsic semiparametric nature. The second aim is to derive a semiparametric lower bound for the estimation of the parametric component of the model. The RES distributions represent a semiparametric model, where the parametric part is given by the mean vector and by the scatter matrix, while the non-parametric, infinite-dimensional, part is represented by the density generator. Since, in practical applications, we are often interested only in the estimation of the parametric component, the density generator can be considered as nuisance. The first part of this paper is dedicated to conveniently place the RES distributions in the framework of the semiparametric group models. In the second part, building on the mathematical tools previously introduced, the constrained semiparametric Cramér–Rao bound (CSCRB) for the estimation of the mean vector and of the constrained scatter matrix of a RES distributed random vector is introduced. The CSCRB provides a lower bound on the mean squared error of any robust $M$ -estimator of mean vector and scatter matrix when no a priori information on the density generator is available. A closed-form expression for the CSCRB is derived. Finally, in simulations, we assess the statistical efficiency of the Tyler's and Huber's scatter matrix $M$ -estimators with respect to the CSCRB. [ABSTRACT FROM AUTHOR]

The paper presents a communication efficient distributed algorithm, $\mathcal {CIRFE}$ of the consensus+innovations type, to estimate a high-dimensional parameter in a multi-agent network, in which each agent is interested in reconstructing only a few components of the parameter. This problem arises, for example, when monitoring the high-dimensional distributed state of a large-scale infrastructure with a network of limited capability sensors and where each sensor is tasked with estimating some local components of the state. At each observation sampling epoch, each agent updates its local estimate of the parameter components in its interest set by simultaneously processing the latest locally sensed information (innovations) and the parameter estimates from agents (consensus) in its communication neighborhood given by a time-varying possibly sparse graph. Under minimal conditions, on the interagent communication network and the sensing models, almost sure convergence of the estimate sequence at each agent to the components of the true parameter in its interest set is established. Furthermore, the paper establishes the performance of $\mathcal {CIRFE}$ in terms of asymptotic covariance of the estimate sequences and specifically characterizes the dependencies of the component wise asymptotic covariance in terms of the number of agents tasked with estimating it. Finally, simulation experiments demonstrate the efficacy of $\mathcal {CIRFE}$. [ABSTRACT FROM AUTHOR]

This paper proposes an online condition monitoring system for aluminum electrolytic capacitors used as dc-link capacitors in industrial power converters. Electrolytic capacitors are one of the most fragile components of a power converter system because of their wear-out failures and short lifespan. Therefore, the use of condition monitoring systems for aluminum electrolytic capacitors allow preventive maintenance to be performed, thereby avoiding unexpected shutdown and downtime. The dominant wear-out mechanisms in aluminum electrolytic capacitors are characterized by the increase in equivalent series resistance. Hence, the condition monitoring system proposed in this paper is based on the online estimation of the capacitor's equivalent series resistance using the switching frequency components of the dc-link capacitor voltage and current. The equivalent series resistance estimation method is tested using the PSpice circuit simulator and is validated experimentally using a custom-made inverter. Finally, the equivalent series resistance estimation method is adapted for the condition monitoring of capacitors in industrial power converters and the proposed condition monitoring system is implemented in a commercial ac drive system to prove the applicability of the proposed method to industrial power converters. [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]

The paper presents research results on a novel algorithm that allows estimation of parameters associated with the specific voltage fluctuation sources in the power grid, which change their operating state with a frequency of up to 150 Hz. The proposed algorithm allows estimating the rate of changes of the disturbing load state, the amplitude, and shape of voltage changes caused by specific sources of disturbances. The decomposition of the estimated AM modulation voltage signal in the power grid is used to implement the algorithm. The algorithm uses a carrier function estimator, which allows research on disturbing loads that change their operating state with a frequency greater than the power frequency. The enhanced empirical wavelet transform is used for the decomposition, which allows recreating the selected parameters, considering random operation of disturbing loads. Accurate recreation of selected parameters will allow selective location of disturbing loads by assessing the propagation of voltage fluctuations caused by specific sources of disturbances. An alternative algorithm has been proposed with coding of voltage changes to voltage fluctuation indices determined from the estimated AM modulation signal of voltage, allowing reducing the necessary memory of the measuring device. Experimental studies have been conducted for the proposed algorithms, considering rapid voltage changes. [ABSTRACT FROM AUTHOR]

In recent years, with the improvements in machine learning, image forensics has made considerable progress in detecting editing manipulations. This progress also raises more questions in image forensics research, such as can the parameters applied in a manipulation be estimated. Many parameter estimation works have already been performed. However, most of these works are based on mathematical analyses. In this paper, we attempt to solve a particular parameter estimation problem from a different aspect. Specifically, a new convolutional neural network (CNN) model is proposed to estimate the resampling rate for resampled images regardless of whether the image is upscaled or downscaled. This model features an original layer to generate a measurable energy map toward the estimation of resampling rate (METEOR). The METEOR layer is demonstrated to be an outstanding method that can assist in enhancing the estimation performance of the CNN. Furthermore, the METEOR layer can also increase the robustness of the CNN against JPEG compression, which makes it extremely important in realistic application scenarios. Our work has verified that machine learning, particularly CNNs, with proper optimization can also be refined to adapt to parameter estimation in digital forensics with excellent performance and robustness. [ABSTRACT FROM AUTHOR]

Anisotropic synchronous motors are arousing a renewed interest in many civil and industrial applications. Compared with their isotropic counterpart, they suffer a more complex magnetic structure whose identification requires the knowledge of the stator resistance. While there are many papers about the resistance estimation, it is fair to say that most of them either require the knowledge of other parameters or do not meet the specifications on accuracy and online tracking. This paper proposes a new injection-based method for the online estimation and tracking of the stator resistance, especially suited for anisotropic motors. The usual injection procedure is reversed, i.e., there is a current injection followed by a voltage measurement. It is demonstrated that the method features a resistance measurement with an improved signal-to-noise ratio and does not involve the a priori knowledge of any other motor parameter. The method can be easily applied to any kind of synchronous motor. This paper includes both the mathematical foundations and several experimental results on a laboratory prototype. [ABSTRACT FROM AUTHOR]

Bilinear and Volterra models are important when dealing with nonlinear systems which arise in several signal processing applications. The former can approximate a large class of systems affine in the input with relatively low parametric complexity. Such an approximate bilinear model can be derived by means of Carleman bilinearization (CB). Then, a Volterra model can be computed from it, having the advantage of being linear in the parameters, but often involving a large number of them. In this paper, we develop efficient routines for CB and for computing the Volterra kernels of a bilinear system. We argue that they are useful for studying a class of systems for which a reference physical model is known. In particular, the so-derived kernels allow assessing the suitability of a Volterra filter and of other alternatives for modeling the system of interest. Techniques exploiting sparsity and low rank of involved matrices are proposed for alleviating computing cost. Several examples are given along the paper to illustrate their use, based on existing physical models of loudspeakers. [ABSTRACT FROM AUTHOR]

Several techniques have been developed to estimate the load parameters in power systems. Most of the existing algorithms mainly focus on estimating the parameters for offline studies. With on-going smart grid development, high-resolution data at faster rates are available to allow estimation of load parameters in real time. This paper addresses the challenges in online estimation of the load parameters using phasor measurement unit data. A novel adaptive search-based algorithm to estimate load model parameters is presented here. In this paper, a static load model is used with the Z (constant impedance), I (constant current), and P (constant power) components of the load. Developed estimation algorithms for the ZIP parameter estimation are validated using the IEEE 14-bus system and data provided by the industry collaborators. Simulation results demonstrate the accurate estimation of the ZIP load model using the developed method. Also, various techniques to eliminate anomalies in the input data for accurate estimation of the load parameters have been presented in this paper. [ABSTRACT FROM AUTHOR]

In dipole-antenna array signal-processing algorithm development, research has focused on “short dipoles” whose physical length ( $L$ ) is under (1/10) of a wavelength \lambda $ . Such electrically “short” dipoles have very small input impedances, rendering such “short” dipoles to be inefficient radiators. Practical dipoles, with an electrical length of ({L}/{\lambda }) \in [{0.1, 1}] , have notably larger input impedance, hence making them better radiators. Of such practical dipoles, this paper investigates their use for azimuth-polar direction finding and for polarization estimation. This paper will first present the measurement model (i.e., array manifold) of a triad of such practical dipoles, collocated in space and orthogonally oriented. This paper will then develop the corresponding closed-form algorithms to estimate the bivariate azimuth–elevation direction-of-arrival or the bivariate polarization. Such closed-form algorithms previously have been unavailable in the existing literature for such a triad of electrically “long” dipoles of pragmatic radiation efficiency. [ABSTRACT FROM AUTHOR]

Objective: the purpose of this paper is to propose an optimal control problem formulation to estimate subject-specific Hill model muscle–tendon (MT-) parameters of the knee joint actuators by optimizing the fit between experimental and model-based knee moments. Additionally, this paper aims at determining which sets of functional motions contain the necessary information to identify the MT-parameters. Methods: the optimal control and parameter estimation problem underlying the MT-parameter estimation is solved for subject-specific MT-parameters via direct collocation using an electromyography-driven musculoskeletal model. The sets of motions containing sufficient information to identify the MT-parameters are determined by evaluating knee moments simulated based on subject-specific MT-parameters against experimental moments. Results: the MT-parameter estimation problem was solved in about 30 CPU minutes. MT-parameters could be identified from only seven of the 62 investigated sets of motions, underlining the importance of the experimental protocol. Using subject-specific MT-parameters instead of more common linearly scaled MT-parameters improved the fit between inverse dynamics moments and simulated moments by about 30% in terms of the coefficient of determination (from \text0.57 \pm \text0.20 to \text0.74 \pm \text0.14) and by about 26% in terms of the root mean square error (from \text15.98 \pm \text6.85 to \text11.85 \pm \text4.12\,\textN \cdot \textm ). In particular, subject-specific MT-parameters of the knee flexors were very different from linearly scaled MT-parameters. Conclusion: we introduced a computationally efficient optimal control problem formulation and provided guidelines for designing an experimental protocol to estimate subject-specific MT-parameters improving the accuracy of motion simulations. Significance: the proposed formulation opens new perspectives for subject-specific musculoskeletal modeling, which might be beneficial for simulating and understanding pathological motions. [ABSTRACT FROM AUTHOR]

This paper presents an instrument for finding the location of an object in 3-D space. The location is determined using estimates of the object’s distance from static anchor points with known coordinates. This process involves three main challenges: 1) a way of measuring the Euclidean distance between two points; 2) estimating system parameters such as the position of the anchor points, the speed of sound, and data acquisition delay; and 3) computing the position of the object from its distance to the anchor points. This paper addresses each of these challenges. A wideband acoustic signal is used for distance estimation. Nonlinear regression techniques are used to estimate the positions of the anchors and jointly optimize other system parameters. After calibration, a similar method is also used, this time using the optimized parameter set, to estimate the position of the target object. The design and performance of a prototype system, based on the proposed method, are discussed. The prototype, using five anchors and 13 reference locations, achieved a mean positioning accuracy of 0.86 mm over a test volume spanning 0.5 m $\times0.5$ m $\times0.25$ m. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of parameter estimation for a Fluctuating Two-Ray (FTR) model in the context of wireless mobile communications. Precisely, the received signal is assumed to be the superposition of two dominant components (typically a direct plus a reflected path signal) in addition to diffusive secondary contributions. The signal components may be affected by random amplitude shadowing, statistically modeled by Nakagami- $m$ distribution, multiplied by unknown scaling factors with random uniform independent phases, whereas the diffusive component is assumed to follow the complex Gaussian distribution. Exploiting the method of moments, a $4\times 4$ nonlinear system is herein mathematically derived, which is very hard to be solved due to the strong nonlinearity. Therefore, a sequential procedure based on some prior information about the diffusive component power level is devised to solve it. The effectiveness of the proposed estimation technique is shown by evaluating the normalized root mean square errors as well as mean errors and standard deviations in several operating conditions of practical interest also considering the limit case of only one-ray in order to compare the proposed approach to simpler estimators, already presented in the literature. The results show the robustness of the new estimator even under a multipath model mismatch. Finally, the effectiveness of the proposed estimation procedure is confirmed through measured mmWave data. [ABSTRACT FROM AUTHOR]

System inertia plays a vital role in controlling the angular stability of the system during a disturbance. Due to increased penetration of power electronic interfaced sources, such as Solar Photovoltaic (SPV) source, the overall system inertia reduces and varies depending on their operating conditions. In this paper, an approach for online inertia estimation in the power system network with SPV sources is proposed, using the synchronized measurements from Phasor Measurement Units (PMUs). An equivalent swing equation is used to emulate the network dynamics. A relationship between the inertia constant and the roots of this equation is determined. In order to numerically obtain the roots, the Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) method is first used to find the modes present in the frequency signal. A new formulation is proposed to extract an equivalent mode from all the obtained modes. Also, to avoid phase step error, Rate Of Change Of Frequency (ROCOF) is estimated from the equivalent mode of the frequency signal. Results obtained for the 39 bus New England system for various test cases, using Real-Time Digital Simulator (RTDS), prove the efficacy and superiority of the proposed approach over the existing approaches in the literature. [ABSTRACT FROM AUTHOR]

In general, the hidden Markov random field (HMRF) represents the class label distribution of an image in probabilistic model based segmentation. The class label distributions provided by existing HMRF models consider either the number of neighboring pixels with similar class labels or the spatial distance of neighboring pixels with dissimilar class labels. Also, this spatial information is only considered for estimation of class labels of the image pixels, while its contribution in parameter estimation is completely ignored. This, in turn, deteriorates the parameter estimation, resulting in sub-optimal segmentation performance. Moreover, the existing models assign equal weightage to the spatial information for class label estimation of all pixels throughout the image, which, create significant misclassification for the pixels in boundary region of image classes. In this regard, the paper develops a new clique potential function and a new class label distribution, incorporating the information of image class parameters. Unlike existing HMRF model based segmentation techniques, the proposed framework introduces a new scaling parameter that adaptively measures the contribution of spatial information for class label estimation of image pixels. The importance of the proposed framework is depicted by modifying the HMRF based segmentation methods. The advantage of proposed class label distribution is also demonstrated irrespective of the underlying intensity distributions. The comparative performance of the proposed and existing class label distributions in HMRF model is demonstrated both qualitatively and quantitatively for brain MR image segmentation, HEp-2 cell delineation, natural image and object segmentation. [ABSTRACT FROM AUTHOR]

Stereo videos for the dynamic scenes often show unpleasant blurred effects due to the camera motion and the multiple moving objects with large depth variations. Given consecutive blurred stereo video frames, we aim to recover the latent clean images, estimate the 3D scene flow and segment the multiple moving objects. These three tasks have been previously addressed separately, which fail to exploit the internal connections among these tasks and cannot achieve optimality. In this paper, we propose to jointly solve these three tasks in a unified framework by exploiting their intrinsic connections. To this end, we represent the dynamic scenes with the piece-wise planar model, which exploits the local structure of the scene and expresses various dynamic scenes. Under our model, these three tasks are naturally connected and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes). By exploiting the blur model constraint, the moving objects and the 3D scene structure, we reach an energy minimization formulation for joint deblurring, scene flow and segmentation. We evaluate our approach extensively on both synthetic datasets and publicly available real datasets with fast-moving objects, camera motion, uncontrolled lighting conditions and shadows. Experimental results demonstrate that our method can achieve significant improvement in stereo video deblurring, scene flow estimation and moving object segmentation, over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Efficient and accurate estimation of voltages and currents is essential for the control of electrical grids, particularly microgrids. In this paper, we present a novel two-step framework to estimate these parameters for both single-phase and three-phase power systems including harmonics and interharmonics. After employing a harmonic version of the Aboutanios and Mulgrew (HAM) estimator, the fundamental frequency and phase estimates are further refined by a weighted least squares (WLS) estimator. The WLS refinement step takes advantage of the harmonic and interharmonic relationships to further improve the parameter estimation from the HAM step. Consequently, the proposed HAM-WLS method maintains accuracy even in electrically-noisy environments that are also contaminated with both harmonics and interharmonics. Results from both simulated and real data under various power system conditions verify that the HAM-WLS estimator outperforms state-of-the-art estimation methods in terms of accuracy, complexity and convergence. [ABSTRACT FROM AUTHOR]