Parameter Estimation Algorithm Research Articles

Initial value compensation of an adaptive fractional-order unscented Kalman filter for estimating the state of charge of lithium-ion batteries

This study indicates an adaptive fractional-order unscented Kalman filter (UKF) with unknown parameters and order, which provides a higher accuracy of SOC estimation. In order to solve the problem of reduced estimation accuracy caused by the low order in the adaptive estimation, this paper adopts the augmented vector method for the initial value compensation (IVC) to reduce the impact of initial values on the estimation accuracy. Because the parameters of the LIB change with the charge and discharge of the battery use and the working conditions, and to increase the ability of the adaptive SOC estimation, this paper studies an adaptive unscented Kalman filter (AUKF) algorithm for the joint estimation of state, parameters, and order of model based on IVC. The algorithm can achieve the adaptive estimation of SOC under different working conditions. Finally, several sets of experiments are conducted under different working conditions to verify that the AUKF algorithm with IVC can achieve the effective estimation of SOC, and the adaptive effect and the estimation error of IVC on the algorithm are better than those without IVC.

A species richness estimator for sample‐based incidence data sampled without replacement

Abstract The accurate estimation of species richness in a target region is still a statistical challenge, especially in a highly heterogeneous community. Most richness estimators have been developed based on the assumption that data are randomly sampled with replacement or that data are sampled from an infinite population. However, in reality, most sampling schemes in the field are implemented as sampling without replacement (SWOR). As such, estimators derived based on sampling with replacement may cause overestimation as the sampling fraction increases and not converge to the true richness as the sampling fraction approaches one. Sample‐based incidence data, in which the sampling unit is a plot, and only the presence or absence of a species in each chosen plot is recorded, is one of the most commonly used data types for assessing species diversity in ecological studies. In this manuscript, according to sample‐based incidence data collected through SWOR, a new richness estimator is proposed using a truncated beta‐binomial mixture model. The new estimator was obtained through the moment approach, which avoids using iterative numerical algorithms for parameter estimation and presents a closed‐form estimator as an alternative to the maximum likelihood method. Although the newly proposed method is a parametric‐based richness estimator, similar to nonparametric estimators, only the rare species in the sample (i.e. the frequencies of uniques and duplicates) are required to estimate undetected richness. Based on the hypothetical models, the statistical performances of the proposed estimator are evaluated under varying degrees of heterogeneity and different mean species detection rates. Compared to other widely used nonparametric and parametric estimators, the simulation results indicate that the proposed estimator has a smaller bias and lower root mean square error when the sampling fraction is greater than 10%, particularly in highly heterogeneous communities. In addition, one ForestGEO permanent forest plot dataset is used to evaluate and compare the proposed approach with other estimators discussed in the study. The results demonstrate that the proposed estimator, in comparison to other widely used estimators, produces less biased estimate of true richness, along with more accurate 95% confidence interval.

Forecasting the Indonesian Coffee Production and Consumption Using the Modified Golden Section Search to Estimate the Smoothing Parameters

The Double Exponential Smoothing (DES) and Triple Exponential Smoothing (TES) are forecasting methods that require two and three smoothing parameters, respectively. Smoothing parameters are often determined through a trial and error process that is not really efficient since many experiments need to be done. Therefore, in this study, a smoothing parameter estimation algorithm is conducted in the form of the modified Golden Section Search (GSS) to obtain the optimal smoothing parameters from the DES and TES methods. Forecasting is carried out on production, domestic consumption, and export consumption data of Indonesian coffee, which is one of the leading agricultural sub-sector commodities. The data is obtained from the Ministry of Agriculture of the Republic of Indonesia. The smoothing parameters obtained by applying the modified GSS are used to forecast production and domestic consumption data using the DES method, while the forecasting of the export consumption data is done with the TES method. All of the MAPE values are less than 20% which indicates that the smoothing parameters obtained by using the modified GSS are able to perform good forecasting. The results show that coffee production in Indonesia cannot meet its demand until 2024 since the total coffee consumption exceeds the production.

Forecasting the Indonesian Coffee Production and Consumption Using the Modified Golden Section Search to Estimate the Smoothing Parameters

The Double Exponential Smoothing (DES) and Triple Exponential Smoothing (TES) are forecasting methods that require two and three smoothing parameters, respectively. Smoothing parameters are often determined through a trial and error process that is not really efficient since many experiments need to be done. Therefore, in this study, a smoothing parameter estimation algorithm is conducted in the form of the modified Golden Section Search (GSS) to obtain the optimal smoothing parameters from the DES and TES methods. Forecasting is carried out on production, domestic consumption, and export consumption data of Indonesian coffee, which is one of the leading agricultural sub-sector commodities. The data is obtained from the Ministry of Agriculture of the Republic of Indonesia. The smoothing parameters obtained by applying the modified GSS are used to forecast production and domestic consumption data using the DES method, while the forecasting of the export consumption data is done with the TES method. All of the MAPE values are less than 20% which indicates that the smoothing parameters obtained by using the modified GSS are able to perform good forecasting. The results show that coffee production in Indonesia cannot meet its demand until 2024 since the total coffee consumption exceeds the production.

Sensing RISs: Enabling Dimension-Independent CSI Acquisition for Beamforming

Reconfigurable intelligent surfaces (RISs) are envisioned as a potentially transformative technology for future wireless communications. However, RISs' inability to process signals and the attendant increased channel dimension have brought new challenges to RIS-assisted systems, including significantly increased pilot overhead required for channel estimation. To address these problems, several prior contributions that enhance the hardware architecture of RISs or develop algorithms to exploit the channels' mathematical properties have been made, where the required pilot overhead is reduced to be proportional to the number of RIS elements. In this paper, we propose a dimension-independent channel state information (CSI) acquisition approach in which the required pilot overhead is independent of the number of RIS elements. Specifically, in contrast to traditional signal transmission methods, where signals from the base station (BS) and the users are transmitted in different time slots, we propose a novel method in which signals are transmitted from the BS and the user simultaneously during CSI acquisition. With this method, an electromagnetic interference random field (IRF) will be induced on the RIS, and we propose the structure of sensing RIS to capture its features. Moreover, we develop three algorithms for parameter estimation in this system, in which one of the proposed vM-EM algorithm is analyzed with the fixed-point perturbation method to obtain an asymptotic achievable bound. In addition, we also derive the Cram\'er-Rao lower bound (CRLB) and an asymptotic expression for characterizing the best possible performance of the proposed algorithms. Simulation results verify that our proposed signal transmission method and the corresponding algorithms can achieve dimension-independent CSI acquisition for beamforming.

Parameter Estimation Algorithm of Frequency-Hopping Signal in Compressed Domain Based on Improved Atomic Dictionary.

This paper considers the problem of estimating the parameters of a frequency-hopping signal under non-cooperative conditions. To make the estimation of different parameters independently of each other, a compressed domain frequency-hopping signal parameter estimation algorithm based on the improved atomic dictionary is proposed. By segmenting and compressive sampling the received signal, the center frequency of each signal segment is estimated using the maximum dot product. The signal segments are processed with central frequency variation using the improved atomic dictionary to accurately estimate the hopping time. We highlight that one superiority of the proposed algorithm is that high-resolution center frequency estimation can be directly obtained without reconstructing the frequency-hopping signal. Additionally, another superiority of the proposed algorithm is that hopping time estimation has nothing to do with center frequency estimation. Numerical results show that the proposed algorithm can achieve superior performance compared with the competing method.

A composite adaptive robust control for pneumatic servo systems with time-varying inertia

This article proposes a composite adaptive robust control for a pneumatic servo system with time-varying inertia to achieve a good tracking performance. The proposed design not only preserves the merits of both direct adaptive robust control and indirect adaptive robust control but also obtains a better performance of the working condition with time-varying inertias. Firstly, prescribed tracking performance is guaranteed without knowing the bounds of parametric and non-parametric uncertainties by leveraging a discontinuous projection and deterministic robust control technique. And a nonlinear tracking differentiator is used to obviate differential signal in backstepping. Secondly, the online parametric estimation algorithm is developed by intelligently integrating tracking error dynamics and physical plant dynamics. By effectively using the information of both tracking error and prediction error, a high adaptation gain can be used for achieving a faster parameter convergence and smaller tracking error. Moreover, exponential convergence of both tracking error and prediction error can be obtained under certain conditions. Lastly, comparative experiments on different conditions are carried out for a single-rod pneumatic cylinder driven by a proportional directional valve to demonstrate the superiority of the proposed method.

An Improved High-Resolution Parameter Estimation Algorithm Incorporating Beamforming Techniques

When the conventional Space-Alternating Generalized Expectation-maximization (SAGE) algorithm is used to estimate the incident angle of the signal received by an antenna array with fixed aperture, the accuracy and resolvability of angle estimation are affected and limited by the aperture of the antenna array. Therefore, in order to improve the angle estimation accuracy and resolvability beyond the limitation, we propose a beamforming (BF)-incorporated SAGE (BFI-SAGE) algorithm, which is a SAGE-based improved high-resolution parameter estimation (HRPE) algorithm, and it steers the orientation of beam utilizing a predefined BF weighting matrix. A non-uniform scanning mode is determined to achieve higher angle estimation accuracy in the angle range of interest, and the analysis of the angle estimation performance is performed for the cases of uniform and non-uniform scanning modes, respectively. The resolvability performance of the algorithm is also discussed.

Distributed Adaptive Fuzzy Modeling for Industrial Gas–Electricity Networks With Multioperation Modes

Industrial gas-electricity networks typically operate in several modes due to large demand variations. In this study, the nonlinear spatio-temporal transient phenomenon of gas networks (GN) is analyzed by a novel modeling approach, named Takagi-Sugeno fuzzy linear graph (TSFLG), and then a parameter space decomposition (PSD)-based adaptive parameter estimation algorithm is developed for greater efficiency. By merging TS fuzzy theory, the nonlinear terms are replaced by a set of linear elements interpolated by membership functions, which are then connected in series to construct the TSFLG structure. The GN is partitioned into several subnetworks for parallel estimation, and the coupling parameters are determined by the covariance intersection fusion rule. Within each subnetwork, efficiency is further improved by estimating alternately linear and nonlinear parameters. The proposal is assessed in an industrial GN with 12 nodes. Test results indicate that the proposed improves efficiency by 38.64<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> while maintaining accuracy.

A joint and simultaneous prediction framework of weekday and weekend daily-activity travel pattern using conditional dependency networks

Daily activity pattern (DAP) prediction models within the Activity-based Modelling paradigm are being currently developed without adequate consideration of the various interdependencies among activities within a multi-day planning horizon. We hereby propose a conditional dependency network structure based interdependent multilabel-multiclass classification framework for joint and simultaneous prediction of weekday and weekend DAP of an individual. The prime advantage of the proposed modelling framework is flexibility of application of any algorithm for parameter estimation. Random Forest Decision Tree (RFDT), eXtreme Gradient Boosting and Light Gradient Boosting Machine (LightGBM) as the base classifier and probabilistic and non-probabilistic inference approaches are explored for measuring their comparative performance to provide insights for future researchers. Several variables representing neighbourhood characteristics are also investigated as DAP determinants along with socio-economic characteristics of individuals for the first time.This model is estimated based on two-days (weekday and weekend) activity-travel diary of 1808 households (6521 individuals) in Bidhanangar Municipal Corporation, India. The non-probabilistic approach-based models are found to achieve higher accuracy (0.81–0.92) compared to probabilistic models (0.76 to 0.82). RFDT and LightGBM are found to be the best performers in the probabilistic and non-probabilistic frameworks respectively. External validation results show that all proposed multiday-interdependent models (80%-94%) perform better than independent models (64%-83%).This framework can be applied to other transportations planning problems like household interaction in activity generation, joint destination and mode choice. This is also one of the first attempts to investigate the determinants of DAPs of urban commuters in an emerging country like India.

Longitudinal Data Analysis Based on Bayesian Semiparametric Method

A Bayesian semiparametric model framework is proposed to analyze multivariate longitudinal data. The new framework leads to simple explicit posterior distributions of model parameters. It results in easy implementation of the MCMC algorithm for estimation of model parameters and demonstrates fast convergence. The proposed model framework associated with the MCMC algorithm is validated by four covariance structures and a real-life dataset. A simple Monte Carlo study of the model under four covariance structures and an analysis of the real dataset show that the new model framework and its associated Bayesian posterior inferential method through the MCMC algorithm perform fairly well in the sense of easy implementation, fast convergence, and smaller root mean square errors compared with the same model without the specified autoregression structure.

A target parameter estimation algorithm for integration of radar and communication based on orthogonal time frequency space

AbstractOrthogonal 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.

Adaptive Multi-Parameter Estimation of Inertial Stabilization Platform with Unknown Load

In order to improve the state monitoring and adaptive control capability of inertial stabilization platforms (ISPs) with unknown loads, it is necessary to estimate the dynamic parameters comprehensively online. However, most current online estimation methods regard the system as a linear dual-inertia model which neglects the backlash and nonlinear friction torque. It reduces the accuracy of the model and leads to incomplete and low accuracy of the estimated parameters. The purpose of this research is to achieve a comprehensive and accurate online estimation of multiple parameters of ISPs and lay a foundation for state monitoring and adaptive control of ISPs. First, a dual-inertia model containing backlash and nonlinear friction torque of the motor and load is established. Then, the auto-regressive moving average (ARMA) model of the motor and load is established by the forward Euler method, which clearly expresses the online identification formula of the parameters. On this basis, the adaptive identification method based on the recursive extended least squares (RELS) algorithm is used to realize the online estimation of multiple parameters. The simulation and experimental results show that the proposed adaptive multi-parameter estimation method can realize the simultaneous online identification of the moment of inertia of the load, the damping coefficient of motor and load, the transmission stiffness, the Coulomb friction torque of motor and load, and the backlash, and the steady-state error is less than 10%. Compared with the traditional linear dual-inertia model, the similarity between the model based on the proposed adaptive parameter estimation algorithm and the actual system is increased by 65.3%.

An Explicit Form With Continuous Attribute Profile of the Partial Mastery DINA Model

Cognitive diagnosis models (CDMs) are the statistical framework for cognitive diagnostic assessment in education and psychology. They generally assume that subjects’ latent attributes are dichotomous—mastery or nonmastery, which seems quite deterministic. As an alternative to dichotomous attribute mastery, attention is drawn to the use of a continuous attribute mastery format in recent literature. To obtain subjects’ finer-grained attribute mastery for more precise diagnosis and guidance, an equivalent but more explicit form of the partial-mastery-deterministic inputs, noisy “and” gate (DINA) model (termed continuous attribute profile [CAP]-DINA form) is proposed in this article. Its parameters estimation algorithm based on this form using Bayesian techniques with Markov chain Monte Carlo algorithm is also presented. Two simulation studies are conducted then to explore its parameter recovery and model misspecification, and the results demonstrate that the CAP-DINA form performs robustly with satisfactory efficiency in these two aspects. A real data study of the English test also indicates it has a better model fit than DINA.

Adaptive measurement filter: efficient strategy for optimal estimation of quantum Markov chains

Continuous-time measurements are instrumental for a multitude of tasks in quantum engineering and quantum control, including the estimation of dynamical parameters of open quantum systems monitored through the environment. However, such measurements do not extract the maximum amount of information available in the output state, so finding alternative optimal measurement strategies is a major open problem. In this paper we solve this problem in the setting of discrete-time input-output quantum Markov chains. We present an efficient algorithm for optimal estimation of one-dimensional dynamical parameters which consists of an iterative procedure for updating a `measurement filter&amp;apos; operator and determining successive measurement bases for the output units. A key ingredient of the scheme is the use of a coherent quantum absorber as a way to post-process the output after the interaction with the system. This is designed adaptively such that the joint system and absorber stationary state is pure at a reference parameter value. The scheme offers an exciting prospect for optimal continuous-time adaptive measurements, but more work is needed to find realistic practical implementations.

A FRAMEWORK FOR FUNCTIONAL PARTIALLY LINEAR SINGLE-INDEX MODELS: FORMULATION AND ANALYSIS

This paper introduces a framework for functional partially linear single-index models, offering a versatile approach for analyzing complex data structures. Functional data analysis techniques are combined with partially linear single-index models to accommodate the inherent variability and nonlinearity present in functional data. The proposed framework allows for the incorporation of both functional and scalar covariates, enabling a comprehensive analysis of multidimensional datasets. We present the formulation of the model, detailing the integration of functional components and linear indices, and discuss computational algorithms for parameter estimation and inference. The effectiveness of the framework is demonstrated through simulation studies and applications to real-world datasets, highlighting its flexibility and utility in capturing intricate relationships and patterns within data.

Mean-field model of synchronization for open-loop, swirl controlled thermoacoustic system.

Open-loop control is known to be an effective strategy for controlling self-excited periodic oscillations, known as thermoacoustic instability, in turbulent combustors. Here, we present experimental observations and a synchronization model for the suppression of thermoacoustic instability achieved by rotating the otherwise static swirler in a lab-scale turbulent combustor. Starting with the state of thermoacoustic instability in the combustor, we find that a progressive increase in the swirler rotation rate leads to a transition from the state of limit cycle oscillations to the low-amplitude aperiodic oscillations through a state of intermittency. To model such a transition while also quantifying the underlying synchronization characteristics, we extend the model of Dutta et al. [Phys. Rev. E 99, 032215 (2019)] by introducing a feedback between the ensemble of phase oscillators and the acoustic. The coupling strength in the model is determined by considering the effect of the acoustic and swirl frequencies. The link between the model and experimental results is quantitatively established by implementing an optimization algorithm for model parameter estimation. We show that the model is capable of replicating the bifurcation characteristics, nonlinear features of time series, probability density function, and amplitude spectrum of acoustic pressure and heat release rate fluctuations at various dynamical states observed during the transition to the state of suppression. Most importantly, we discuss the flame dynamics and demonstrate that the model without any spatial inputs qualitatively captures the characteristics of the spatiotemporal synchronization between the local heat release rate fluctuations and the acoustic pressure that underpins a transition to the state of suppression. As a result, the model emerges as a powerful tool for explaining and controlling instabilities in thermoacoustic and other extended fluid dynamical systems, where spatiotemporal interactions lead to rich dynamical phenomena.

Physical-layer key generation based on multipath channel diversity using dynamic metasurface antennas

Physical layer key generation (PKG) technology leverages the reciprocal channel randomness to generate the shared secret keys. The low secret key capacity of the existing PKG schemes is due to the reduction in degree-of-freedom from multipath fading channels to multipath combined channels. To improve the wireless key generation rate, we propose a multipath channel diversity-based PKG scheme. Assisted by dynamic metasurface antennas (DMA), a two-stage multipath channel parameter estimation algorithm is proposed to efficiently realize super-resolution multi-path parameter estimation. The proposed algorithm first estimates the angle of arrival (AOA) based on the reconfigurable radiation pattern of DMA, and then utilizes the results to design the training beamforming and receive beamforming to improve the estimation accuracy of the path gain. After multipath separation and parameter estimation, multi-dimensional independent path gains are utilized for generating secret keys. Finally, we analyze the security and complexity of the proposed scheme and give an upper bound on the secret key capacity in the high signal-to-noise ratio (SNR) region. The simulation results demonstrate that the proposed scheme can greatly improve the secret key capacity compared with the existing schemes.

PV Panel Model Parameter Estimation by Using Neural Network

Photovoltaic (PV) panels have been widely used as one of the solutions for green energy sources. Performance monitoring, fault diagnosis, and Control of Operation at Maximum Power Point (MPP) of PV panels became one of the popular research topics in the past. Model parameters could reflect the health conditions of a PV panel, and model parameter estimation can be applied to PV panel fault diagnosis. In this paper, we will propose a new algorithm for PV panel model parameters estimation by using a Neural Network (ANN) with a Numerical Current Prediction (NCP) layer. Output voltage and current signals (VI) after load perturbation are observed. An ANN is trained to estimate the PV panel model parameters, which is then fined tuned by the NCP to improve the accuracy to about 6%. During the testing stage, VI signals are input into the proposed ANN-NCP system. PV panel model parameters can then be estimated by the proposed algorithms, and the estimated model parameters can be then used for fault detection, health monitoring, and tracking operating points for MPP conditions.

Motion parameters estimation of manoeuvring weak target with multiple motion stages based on range symmetry transform and short‐time fractional Fourier transform

AbstractWith the advancement of manoeuvrability and the increasement of coherent processing interval (CPI), the observed target might be of multiple motion stages. Therefore, most existing long‐time coherent integration (LTCI) algorithms based on the assumption of single‐motion stage is not proper any more. To address this problem, this letter proposes an effective motion parameters estimation algorithm, which is the key step of LTCI, for the manoeuvring weak target with multiple motion stages based on range symmetry transform and short‐time fractional Fourier transform (RST‐STFrFT). The effectiveness of the proposed algorithm is verified by signal‐level simulations.