Parameter Estimation Algorithm Research Articles

UAV-Aided Radio Map Construction Exploiting Environment Semantics

This paper constructs a full dimensional (6D) radio map to predict the channel gain between any transmitter location and any receiver location based on received signal strength (RSS) measurements between low-altitude aerial nodes and ground nodes. The main challenge is how to describe the signal strength attenuation due to the blockage from the environment. Conventional interpolation-type approaches fail to exploit the close relation between the radio map and the geometry of the environment. This paper proposes to construct radio maps by first estimating and constructing a multi-class 3D virtual obstacle map that describes the geometry of the environment with radio semantics. Mathematically, a least-squares problem is formulated to jointly estimate the virtual obstacle map and the propagation parameters. This problem is found to have a partial quasiconvexity that leads to the development of an efficient parameter estimation and radio map construction algorithm. Numerical experiments confirm that the proposed method substantially reduces the amount of measurement required for achieving the same radio map accuracy. It is also demonstrated that in a unmanned aerial vehicle (UAV)-aided relay communication scenario, a radio map assisted approach for UAV placement can achieve more than 50% capacity gain.

Health Observation of the Capital Airport South Expressway Based on Improved MT-InSAR Technology

Objectives: The Airport South Expressway in China is built in a soft soil area, which may induce great hidden danger to airport traffic safety operations. Technology or Method: A new method to improve Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology, based on a novel time-series InSAR deformation model and an improved parameter estimation algorithm, is proposed for soft soil expressways monitoring. The functional relationship between the deformation and the creep parameters (viscosity and elastic modulus) based on the Maxwell model in one-dimensional linear rheology replaces the traditional InSAR linear model, and the creep physical parameters can be solved simultaneously in the solution process. The least-squares method with inequality constraints (LSIC) is induced to solve the unknown parameters. In total, 19 TerraSAR-X radar satellite images covering the South Expressway were utilized to validate the proposed method. The creep parameters for each pixel along the expressways and the time series deformation sequences from January 2012 to July 2014 were obtained. Results: As the results showed, the maximum settlement along the expressway was up to 125 mm, and the accuracy verification results showed that the modeling accuracy was 1.6 mm, with an improvement of 36.0% compared to the traditional linear model; the internal accuracy of the deformation results was 11.9 mm, accounting for 1.5% of the maximum deformation. Clinical or Biological Impact: Our method can provide data support and a reference for long-term health monitoring and early warning of infrastructure and traffic operation management in poor soil regions.

(Keynote) Inferring Degradation Modes from Battery Cycling Data

Battery research and development efforts necessarily involve large-scale experimental studies where cells are cycled under different conditions. Physics based models (PBM) of the type pioneered by John Newman, Ralph White, and others can play a key role in interpreting the resulting cycling data, and of course can be used to improve performance by modifications of cell design to optimize for a specific application. Long-term studies that characterize degradation in cell performance are an important type of cycling experiment that can be used to estimate useful battery life in a real-world application. The establishment of correlations between duty cycle and life is valuable and can inform battery management systems with a goal to extend life. During the course of such experiments, large data sets are created and we explore how a PBM might be used to infer the modes of cathode degradation. From degradation modes, mechanisms of degradations might be hypothesized, and this may lead to insights to further improve design and operation.Here, two PBM are reformulated to determine how cell parameters such as the amount of active cathode material appears to change with cycle number. Parameter estimation is applied to cycling data, and parameter changes are tracked for cycling data at different C-rates and different cutoff voltages. Parameter estimates are challenged with complementary experimental data for NMC and for a second cathode material that has been characterized extensively. The comparisons suggest that the methodology is effective and can provide physical insights into degradation mechanisms. To facilitate the “workflow,” inverse surrogate machine-learning techniques, in which simulation data are used for training neural networks to rapidly estimate parameters from experimental data, were utilized. To allow for both imperfect PBM and for non-ideal experimental data, data augmentation methods were introduced to improve the accuracy of parameter estimation algorithms. Examples are shown, with an emphasis on the physics over the mathematics and with an eye towards next steps.

Real-time parameter estimation of polymer electrolyte membrane fuel cell in absence of excitation

Parameter estimation is crucial for polymer electrolyte membrane fuel cell monitoring and control. Nonetheless, most parameter estimation algorithms rely on a persistence of excitation condition, which is rarely satisfied and not convenient in fuel cell systems. For this reason, this work presents and compares three algorithms to estimate in real-time some critical PEMFC parameters in the voltage equation: the ohmic resistance, the charge transfer coefficient and the oxygen activity of a proton exchange fuel cell. The first algorithm is a standard gradient descent, while the other two are based on a set of pre-preprocessing dynamics. It is shown that, while the gradient descent requires the persistence of excitation condition, the addition of the pre-processing dynamics ensures reliable estimation under significantly weaker excitation assumptions. Moreover, it is shown that the pre-processing dynamics improves the transient behaviour and noise performance of the estimators. The results are validated through a set of numerical simulations and in an experimental prototype, where sensor noise and unmodelled disturbances are considered.

Ambient oscillatory mode assessment in power system using an advanced signal processing method

Estimation of electromechanical mode properties is crucial in modern power systems for providing the operators with an adequate indication of the stress in the system. Measurement-based approaches use signal processing algorithms for mode identification and parameter estimation. This paper presents a novel framework for the assessment of low-frequency oscillation modes using real-world synchrophasor data with minimum computational effort. A nonstationary approach known as Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) technique is used to identify the dominant low-frequency modes present in the ambient PMU data. The combination of TVF-EMD with Teager Kaiser Energy Operator (TKEO) precisely estimates the instantaneous mode parameters, such as frequency, amplitude, and damping ratio. The efficacy of the proposed approach is demonstrated by applying it in a synthetic signal, simulated data of a standard IEEE test system, and in real-world PMU data of the Indian power grid. The proposed method is compared with the existing methodologies and the observations reveal that the proposed method has robust performance in estimating the instantaneous mode features in the power system with less computational complexities.

Modulatory dynamics of periodic and aperiodic activity in respiration-brain coupling

Bodily rhythms such as respiration are increasingly acknowledged to modulate neural oscillations underlying human action, perception, and cognition. Conversely, the link between respiration and aperiodic brain activity – a non-oscillatory reflection of excitation-inhibition (E:I) balance – has remained unstudied. Aiming to disentangle potential respiration-related dynamics of periodic and aperiodic activity, we applied recently developed algorithms of time-resolved parameter estimation to resting-state MEG and EEG data from two labs (total N = 78 participants). We provide evidence that fluctuations of aperiodic brain activity (1/f slope) are phase-locked to the respiratory cycle, which suggests that spontaneous state shifts of excitation-inhibition balance are at least partly influenced by peripheral bodily signals. Moreover, differential temporal dynamics in their coupling to non-oscillatory and oscillatory activity raise the possibility of a functional distinction in the way each component is related to respiration. Our findings highlight the role of respiration as a physiological influence on brain signalling.

Optimal model parameter estimation and performance analysis of PEM electrolyzer using modified honey badger algorithm

The electrochemical model of a proton exchange membrane (PEM) electrolyzer consists of a set of different unknown parameters, and identification of these parameters is needed. However, obtaining the optimum values of these parameters is critical, and this involves a complex, multi-variate, non-linear, and multimodal optimisation problem. In this article, a novel model parameter estimation problem for a PEM electrolyzer with eight unknown model parameters is formulated and solved using modified honey badger algorithm (MHBA). A mean squared error-based objective function is formulated and considered as the mean squared error (MSE) between the experimental voltage and the estimated voltage using MHBA algorithm. The accuracy of the proposed model is validated using polarization curves (J-V curve). It is found that the MHBA outperforms with a good mapping between experimental and estimated voltage. Also, the reliability and robustness for parameter estimation algorithm is validated considering two case studies with four different operating conditions. Additionally, the results obtained using MHBA are compared to those obtained with other competing algorithms. A statistical study including different statistical indices is also carried out to prove the robustness for the proposed approach. The results show that the minimum obtained values of MSE for different operating conditions are 8.73E-06, 8.75E-06, 6.17E-05 and 6.44E-05. Box plot study and convergence curves have also been drawn to check the effectiveness of the algorithm. Furthermore, to prove the superiority of the proposed approach, the PEM electrolyzer's performance is explored at different operating conditions, such as operating temperatures and output hydrogen pressures.

Neural Partial Differentiation for Parameter Estimation of Flexible Aircraft Dynamics

Although the parameter estimation methodologies had been successfully applied for the rigid aircraft dynamics, the aircraft with high degree of flexibility poses challenges to determine the appropriate aeroelastic models for inclusion in the parameter estimation algorithms. Application of the neural networks for the aerodynamic modeling and parameter estimation of an aeroelastic aircraft (or flexible aircraft) is addressed in this paper. A neural network with the scaled conjugate gradient learning algorithm is used for modeling lift and pitching moment coefficients. The partial differentiation of the neural network output is carried out for the estimation of equivalent parameters of an aeroelastic aircraft. It is demonstrated that a reasonable model and estimates of the equivalent parameters for the aeroelastic aircraft are possible with the neural partial differential method by using the measured motion and control variables, without any need for postulating the coupled equations of motion or initialization of the parameters to be estimated. The proposed approach reduces the large number of parameters for aeroelastic aircraft dynamics into fewer equivalent parameters, and this methodology does not require the measurements of the elastic deflections or their derivatives. The neural partial differential method is applied to the simulated flight data of an aeroelastic aircraft for the longitudinal dynamics. The performance of the neural partial differential approach is compared with the estimates obtained from the other methods like classical estimation methods, analytical computation, and values given in the literature for rigid body modeling. The performance is also validated by comparing the measured lift force and pitching moment coefficients with the neural predicted and neural reconstructed coefficients using the estimates obtained through the neural partial differential approach.

Implementing a novel fault prognosis technique based on nonlinear fault observer and online parameters estimation

<span lang="EN-US">This research presents a methodology for predicting errors of parameters, as the algorithm tries to monitor the parameters in order to maintain or replace them when needed to avoid excessive expenses. The presented implementation mechanism is based on monitoring parameters according to a specific number of batches and each batch consists of a number of iterations, which in turn are a number of samples. The proposed algorithm involves designing a new nonlinear observer and writing a secondary algorithm for parameter estimation based on the online nonlinear recursive least squares algorithm associated with the observer states. In addition, the algorithm presents an attempt to find a relationship between the error states and the state of the parameters by creating a new function to determine the weight of the error according to four components; parameter changes, output residuals, output errors and the error diagnosed by the new observer. The algorithm also includes introducing the probability form of the weights using the kernel density function for the average and maximum weights for each batch. Finally, relying on the results, it is possible to take the appropriate decision to maintain or change the parameters as shown a non-linear direct current motor model case study.</span>

Aircraft Parameter Estimation Using Gaussian Sum Filter With Lyapunov Stability Analysis

The use of Gaussian Sum Filter (GSF) as a state/parameter estimation tool for aerodynamic modelling is relatively unexplored in the literature. In this paper GSF has been employed for aircraft parameter estimation. The stability analysis of GSF has also been discussed using the normalized Lyapunov energy functional and the conditions for the convergence of the GSF are derived by using the proposed nonlinear observer which is a novel approach. An approach to prune the weights of GSF based on Pearson coefficient is considered. The performances of the three recursive parameter estimation algorithms: Extended Kalman Filter (EKF), the simplified version of the GSF and the improved version of the GSF are evaluated for aircraft parameter estimation using simulated flight data in the MATLAB implementations. The numerical results show that pruning the weights reduces the computational load by nearly 75%, and still provides good performance compared to the conventional GSF.

Tension Control in Unwinding System Based on Nonlinear Parameter Estimation

In this paper, two representative nonlinearities in the unwinding system are modelled. One is the rotation-disturbance caused by the rotational change of unwinding roll position; another is the time-varying radius of unwinding roll. Based on the nonlinear mechanism model, the parameters of rotation-disturbance are obtained by the Gauss-Newton algorithm, and the time-varying radius is estimated online by the Kalman filter. Then the feedforward and time-varying feedback controller are designed based on the estimated parameters to compensate for nonlinear effect in unwinding system. The experiment results show that the parameter estimation algorithm can accurately estimate parameters. And the use of estimated parameters to control can effectively solve the problem of tension fluctuation and tension deviation which caused by the nonlinearity of unwinding system.

Distributed parameter identification algorithm for large‐scale interconnected systems

AbstractThis 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.

3D target detection and spectral classification for single-photon LiDAR data.

3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithms.

High-Speed Maneuvering Target Inverse Synthetic Aperture Radar Imaging and Motion Parameter Estimation Based on Fast Spare Bayesian Learning and Minimum Entropy

High-speed maneuvering target inverse synthetic aperture radar (ISAR) imaging is always a hot topic in signal processing. High-speed maneuvering targets often have high-order maneuvering characteristics, such as translational and rotational characteristics, which destroy the signal structure of stationary targets and make basic imaging processing methods such as range-Doppler (RD) algorithm no longer suitable. In this paper, a high-resolution imaging method for high-speed maneuvering targets is proposed, which uses the fast sparse Bayesian learning (SBL) algorithm and the minimum entropy algorithm for ISAR high-resolution imaging and motion parameter estimation, respectively. Because SBL makes full use of the characteristics of the target and the environment, it can obtain an ISAR high-resolution image of the maneuvering target. However, the high computational complexity caused by matrix inversion and some matrix operations in SBL iteration limit the practical application of SBL in ISAR imaging. In view of the special structure of the matrix required to be inverted, we propose a fast SBL algorithm, which uses a new decomposition method to obtain the decomposition formula of the inverse matrix. Based on the decomposition factors, the multiplication operation involving the inverse matrix can be quickly calculated using fast Fourier transform (FFT), which greatly improves the computational efficiency. Image entropy represents the sharpness and focusing degree of an image, and so the minimum entropy algorithm can estimate the motion parameters of maneuvering targets more accurately. We combine the minimum entropy algorithm with the fast SBL algorithm to realize phase error correction and high-resolution imaging, which has better noise sensitivity and can obtain the best focusing degree image. Finally, simulation results prove the effectiveness of this algorithm.

A Gradient-Descent Optimization Assisted Gray-Box Impedance Modeling of EV Chargers

Extracting an electric vehicle (EV) charger's input impedance with the analytical model (white-box approach) or the frequency sweep (black-box approach) is limited by the parameter confidentiality or the measurement noise, respectively. To overcome these challenges, a Gradient-Descent (GD) optimization based gray-box modeling approach is proposed. To start with, a sensitivity study on the analytical impedance model of an EV charger with a typical controller is carried out to identify the influential frequency range per controller and circuit parameter. On top of that, given an EV charger with unknown control and circuit information, a GD optimization based algorithm for multiple parameter estimation is designed to identify the unknown controller and circuit parameters based on the measured impedance, by assuming the EV charger is using the typical controller. Then, an analytical input impedance of the black-box EV charger can be obtained. Moreover, the low accuracy issue commonly encountered when estimating multiple parameters with GD optimization is mitigated with the proposed algorithm. Compared to pure frequency sweep, the proposed approach achieves a higher accuracy for the coupling impedance and a comparable accuracy for the diagonal impedance. The effectiveness of the proposed approach is validated by experimental results.

A modified reptile search algorithm for parametric estimation of fractional order model of lithium battery

AbstractIn this paper, a Levy reptile search algorithm (LRSA) is proposed to improve the global search capability and convergence speed of reptile search algorithm which has advantages in solving single‐modal, multi‐modal and composite problems. Firstly, circle chaotic mapping is introduced to make the initial distribution of population more uniform and diversified. Secondly, Levy flight strategy is employed in the global search, which can improve the accuracy and convergence speed. In order to test and verify the optimization performance of the LRSA, 12 benchmark functions are tested and compared with four other intelligent optimization algorithms. It can be seen that LRSA is effective and advantageous in average convergence speed. In addition, the proposed LRSA is applied to a fractional order model identification of lithium battery with a very small error (less than 2%). The experimental results show that the LRSA can effectively estimate the parameters of the fractional order model and aid to state of charge and state of health estimation.

3D integral imaging depth estimation of partially occluded objects using mutual information and Bayesian optimization.

Integral imaging (InIm) is useful for passive ranging and 3D visualization of partially-occluded objects. We consider 3D object localization within a scene and in occlusions. 2D localization can be achieved using machine learning and non-machine learning-based techniques. These techniques aim to provide a 2D bounding box around each one of the objects of interest. A recent study uses InIm for the 3D reconstruction of the scene with occlusions and utilizes mutual information (MI) between the bounding box in this 3D reconstructed scene and the corresponding bounding box in the central elemental image to achieve passive depth estimation of partially occluded objects. Here, we improve upon this InIm method by using Bayesian optimization to minimize the number of required 3D scene reconstructions. We evaluate the performance of the proposed approach by analyzing different kernel functions, acquisition functions, and parameter estimation algorithms for Bayesian optimization-based inference for simultaneous depth estimation of objects and occlusion. In our optical experiments, mutual-information-based depth estimation with Bayesian optimization achieves depth estimation with a handful of 3D reconstructions. To the best of our knowledge, this is the first report to use Bayesian optimization for mutual information-based InIm depth estimation.

Estimators of tissue absorption parameters power-law prefactor and power-law exponent from medical ultrasonic images

Abstract Ultrasound is a mechanical wave propagating in tissue which is influenced in its propagation behavior by the locally prevailing acousto-mechanical conditions. By suitable processing of the back-scattered signals received by the ultrasound transducer, tissue parameters such as local bulk modulus, mass density, speed of sound, isotropic scattering coefficient, and also the locally acting tissue absorption can be inferred. A discipline that has received increasing attention in the medical ultrasonic imaging discipline and its scientific publications in recent years is quantitative ultrasound (QUS) which tries to estimate with great accuracy these local acting tissue parameters. In this paper we analyze different algorithms for estimation of high spatial resolution tissue absorption parameters. On the one hand, there is a simple absorption estimator based on the evaluation of the quotient of the power density spectra calculated for different depth regions (spectral-log-difference estimator), which, however, assumes a linearly with frequency increasing absorption, this is contrasted with an estimator which also allows to estimate a polynomial increase of the absorption with frequency (method-of-moments estimator). Since a closed-form solution cannot be given for this, a maximum-likelihood estimator for which there is always an estimate that can be computed numerically efficiently is developed. The results, tissue attenuation, are presented as a color-coded overlay on conventional B-mode ultrasound images showing only morphology.

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.