Adaptive Estimation Approach Research Articles

Development of a reliable adaptive estimation approach for a low‐cost attitude and heading reference system

AbstractA main challenge within inertial navigation systems involves attitude determination, which refers to the process of estimating the angles that define orientations. A new algorithm is presented to estimate a reliable and proper model for a low‐cost attitude and heading reference system (AHRS), while also, including long‐term global navigation satellite system (GNSS) outages with various dynamical manoeuvres. In the proposed approach, the initial AHRS model is continuously refined at each time step through the incorporation of complementary terms using a new prediction method. These complementary terms are determined using attitude information from the accelerometers output and the GNSS as reference systems. The proposed estimation technique undergoes mathematical analysis to ascertain stochastic stability and is further assessed through real‐world aerial experimental tests conducted with hardware‐in‐the‐loop mechanisation. The obtained results demonstrate a significant enhancement in the reliability and accuracy of the AHRS through the proposed estimation algorithm, even under GNSS blockages. The comparative results highlight the higher performance of the suggested data fusion scheme in providing a reliable and accurate model for the AHRS in normal conditions and assisting a powerful neural network‐based algorithm to provide reliable heading information even during long‐term GNSS outages under dynamical manoeuvres.

A switching norm based least mean Square/Fourth adaptive technique for sparse channel estimation and echo cancellation

To realize rapid data transmission, the broadband transmission technique is being extensively explored and applied in existing wireless communication systems. The multi-path channel in broadband wireless communication systems is sparse and this sparsity can be used as prior knowledge to estimate the channel. To make use of sparsity, this paper recommends a switching norm-based least mean square/fourth (SN-LMS/F) adaptive approach for sparse channel estimation and echo cancellation. The suggested SN-LMS/F is implemented by adding a soft parameter adjustment function (SPF) into the conventional LMS/F adaptive method's cost function and utilizes both the l0 and l1 norm to exploit system sparsity with reduced complexity. The simulated output indicates that the suggested SN-LMS/F adaptive technique provides a more desirable performance for sparse channel estimation and echo cancellation with reduced execution time.

Adaptive polynomial Kalman filter for nonlinear state estimation in modified AR time series with fixed coefficients

AbstractThis article presents a novel approach for adaptive nonlinear state estimation in a modified autoregressive time series with fixed coefficients, leveraging an adaptive polynomial Kalman filter (APKF). The proposed APKF dynamically adjusts the evolving system dynamics by selecting an appropriate autoregressive time‐series model corresponding to the optimal polynomial order, based on the minimum residual error. This dynamic selection enhances the robustness of the state estimation process, ensuring accurate predictions, even in the presence of varying system complexities and noise. The proposed methodology involves predicting the next state using polynomial extrapolation. Extensive simulations were conducted to validate the performance of the APKF, demonstrating its superiority in accurately estimating the true system state compared with traditional Kalman filtering methods. The root‐mean‐square error was evaluated for various combinations of standard deviations of sensor noise and process noise for different sample sizes. On average, the root‐mean‐square error value, which represents the disparity between the true sensor reading and estimate derived from the adaptive Kalman filter, was 35.31% more accurate than that of the traditional Kalman filter. The comparative analysis highlights the efficacy of the APKF, showing significant improvements in state estimation accuracy and noise resilience.

Human-in-the-Loop Behavior Modeling via an Integral Concurrent Adaptive Inverse Reinforcement Learning.

One goal of artificial intelligence (AI) research is to teach machines how to learn from humans, such that they can perform a certain task in a natural human-like way. In this article, an online adaptive inverse reinforcement learning (IRL) approach to human behavior modeling is proposed to enhance machine intelligence for a class of linear human-in-the-loop (HiTL) systems using the state data only, where the human behavior is described by a linear quadratic optimal control model with an unknown weighting matrix for the quadratic cost function. First, an integral concurrent adaptive law is developed to learn the human feedback gain matrix online using the demonstrated state data only, which removes the persistent excitation (PE) conditions required by traditional adaptive estimation approaches and thus is more in line with real applications. Then, with the learned feedback gain matrix, the IRL problem is formulated as a linear matrix inequality (LMI) optimization problem, which can be efficiently solved to retrieve the weighting matrix of the human cost function. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.

Machine learning based data governance methods for demand response databases

With the increasing level of grid intelligence and the related demand response database expanding, it is important to study a compound problem data governance method for demand response, while the traditional data governance methods have problems such as not considering data temporality and ignoring the impact of noise and duplicate data on data repair. As a result, this project will develop an anomaly data extraction and repair model based on two-way long and short memory networks, and repair the anomaly data by respective noise smoothing, missing data filling, and duplicate data cleaning. The paper also provides an adaptive moment estimation approach for optimisation to raise the model’s accuracy. The outcomes demonstrated that the study model’s precision for anomalous data extraction was 100% and its recall rate was 80%, which was a significant improvement over the previous state. In terms of anomalous data repair, the research model had the root mean square error value and lowest mean absolute percentage error value when compared with related models, at 0.0049 MPa and 1.375% respectively. Both the abnormal data extraction and repair performance of the research model are greatly improved over the related models, and have important value in the abnormal data governance of demand response databases.

Point Cloud Denoising and Feature Preservation: An Adaptive Kernel Approach Based on Local Density and Global Statistics.

Noise removal is a critical stage in the preprocessing of point clouds, exerting a significant impact on subsequent processes such as point cloud classification, segmentation, feature extraction, and 3D reconstruction. The exploration of methods capable of adapting to and effectively handling the noise in point clouds from real-world outdoor scenes remains an open and practically significant issue. Addressing this issue, this study proposes an adaptive kernel approach based on local density and global statistics (AKA-LDGS). This method constructs the overall framework for point cloud denoising using Bayesian estimation theory. It dynamically sets the prior probabilities of real and noise points according to the spatial function relationship, which varies with the distance from the points to the center of the LiDAR. The probability density function (PDF) for real points is constructed using a multivariate Gaussian distribution, while the PDF for noise points is established using a data-driven, non-parametric adaptive kernel density estimation (KDE) approach. Experimental results demonstrate that this method can effectively remove noise from point clouds in real-world outdoor scenes while maintaining the overall structural features of the point cloud.

Improved Adaptive Estimation Approach for Aircraft and Land Vehicle Applications

Improved Adaptive Estimation Approach for Aircraft and Land Vehicle Applications

An adaptive deep multi-task learning approach for citywide travel time collaborative estimation

The provision of accurate travel time information holds paramount significance for optimizing traffic operations and management. However, existing approaches typically address multiple travel time learning tasks independently, neglecting the correlation of tasks and necessitating the construction of separate models for each task. As the scale of the road network expands, this practice becomes increasingly infeasible. To bridge this gap, this paper presents an adaptive deep multi-task learning model to solve multiple travel time estimation tasks collaboratively from a citywide perspective. The novel model seamlessly integrates geographical, environmental, and spatial-correlated features, capturing the complex non-linear characteristics of travel time in the urban road network through a deep network learning feature representations layer by layer from the input data. Through the paradigm of deep multi-task collaborative learning, the knowledge learned from one task serves as an inductive bias for other tasks, thereby facilitating the learning of related tasks. By modeling the uncertainty of travel time with a probabilistic model, the weights of jointly learned tasks are adaptively fine-tuned in a principled way. Additionally, a novel algorithm is devised to fully leverage the information embedded within the data with partial information. The proposed methodology is evaluated through a case study on the citywide road network in Beijing, China. Empirical results of extensive experiments demonstrate the adeptness of the proposed model in effectively harnessing information transferred from parallel tasks, adaptively fine-tuning the weights of joint learning tasks, and proficiently exploiting data characterized by incomplete labels, therefore leading to superior performance compared to competing methods. The average estimation error of the four jointly learned travel time estimation tasks is about 28.48%, yielding an improvement of over 4% compared to single-task learning models. The results verify the effectiveness and robustness of the proposed model for travel time estimation in the urban road network.

Adaptive Parameter Estimation of the Generalized Extreme Value Distribution Using Artificial Neural Network Approach

Parameter estimation strategies have long been a focal point in research due to their significant implications for understanding data behavior, including the dynamics of big data. This study offers an advancement in these strategies by proposing an adaptive parameter estimation approach for the Generalized Extreme Value distribution (GEVD) using an artificial neural network (ANN). Through the proposed adaptive parameter estimation approach, based on ANNs, this study addresses the parameter estimation challenges associated with the GEVD. By harnessing the power of ANNs, the proposed methodology provides an innovative and effective solution for estimating the parameters of the GEVD, enhancing our understanding of extreme value analysis. To predict the flood risk areas in the Chi river watershed in Thailand, we first determine the variables that are significant in estimation of the three GEVD parameters μ,σ, and ξ by considering the respective correlation coefficient and then estimating these parameters. The data were compiled from satellite and meteorological data in the Chi watershed gathered from the Meteorological Department and 92 meteorological stations from 2010 to 2021, and consist of such variables as the Normalized Difference Vegetation Index (NDVI), climate, rainfall, runoff, and so on. The parameter estimation focuses on the GEVD. Taking into consideration that the processes could be stationary (parameters are constant over time, S) or non-stationary (parameters change over time, NS), maximum likelihood estimation and ANN approaches are applied, respectively. Both cases are modeled with the GEVD for the monthly maximum rainfall. The Nash-Sutcliffe coefficient (NSE), is used to compare the performance and accuracy of the models. The results illustrate that the non-stationary model was suitable for 82 stations, while the stationary model was suitable for only 10 stations. The NSE values in each model range from 0.6 to 0.9. This indicated that all 92 models were highly accurate. Furthermore, it is found that meteorological variables, geographical coordinates, and NDVI, that are correlated with the shape parameter in the ANN model, are more significant than others. Finally, two-dimensional maps of the return levels in the 2, 5, 10, 20, 50, and 100-year return periods are presented for further application. Overall, this study contributes to the advancement of parameter estimation strategies in the context of extreme value analysis and offers practical implications for water resource management and flood risk mitigation.

An ASTSEKF optimizer with nonlinear condition adaptability for accurate SOC estimation of lithium-ion batteries

Safe and reliable operations of lithium-ion batteries in electric vehicles (EVs), etc., highly depend on the accurate state of charge (SOC) estimated by the battery management system (BMS). However, due to the battery's nonlinear operating conditions and complex electrochemistry, accurately estimating SOC is a major challenge. In this paper, an adaptive strong tracking square-root extended Kalman filter (ASTSEKF) with nonlinear condition adaptability is proposed for the recursive correction, denoising, and op4timization of the SOC estimation of lithium-ion batteries. The proposed ASTSEKF optimizer introduces an adaptive fading factor, a weight adjustor, and a strong tracking filter to recursively update the posteriori error covariance matrix using a Cholesky decomposition and corrects the uncertainties of the EKF method. The effectiveness of the ASTSEKF method is demonstrated by utilizing it to refine and enhance the estimations of a closed-loop nonlinear autoregressive model with exogenous input (NARX) and a deep feed-forward neural network (DFFNN) utilizing deep transfer learning techniques. The Levenberg-Marquardt and adaptive moment estimation approaches are employed to address gradient issues and stabilize the networks. Battery tests are carried out at different charge-discharge rates, temperatures, complex working conditions, and aging levels. The comparative SOC results show that the proposed ASTSEKF optimizer ensures an overall maximum mean absolute error, mean square error, and root mean square error improvements of 89.82%, 91.67%, and 90.76%, respectively. Additionally, it denoises, stabilizes, optimizes, and quickly converges the final SOC with a good balance between optimal accuracy and computational complexity. In comparison to other existing methods, the proposed ASTSEKF optimizer can overcome nonlinearities encountered by the BMS under various operating conditions to provide accurate SOC estimation in EVs.

Adaptive event-triggered control for uncalibrated visual servoing of robot manipulators

In the study, an adaptive event-triggered control strategy is proposed for image-based visual servoing of eye-to-hand robot manipulators, where the camera used does not need to be calibrated, and the dynamics behavior of manipulator is considered in design and analysis. To address the uncertainty in camera parameters and the nonlinearity in robot dynamics, and accommodate the errors between the real-time control signals and the piecewise-constant event-triggered control signals, a new and novel robust adaptive estimation approach is developed, and well fused with visual feedback control design so that the boundedness of all closed-loop signals, and the asymptotic convergence of image error towards zero are established simultaneously. Besides rigorous proof in theory, the obtained results are also confirmed by comparative simulation tests.

An integrated adaptive Kalman filter for improving the reliability of navigation systems

Abstract Integrated GPS/INS using Kalman filter is the best technique for improving navigation accuracy. Assuming that the covariance matrices are known and constant, a conventional Kalman filter (CKF) is usually used, however, when they are unknown and time-varying, several adaptive estimation approaches have to be developed to estimate the statistical information of the measurement (R), process (Q), and state (P) covariance matrices. In many situations, blunders/faults in the measurement model and/or sudden changes in the dynamic model may occur during the navigation period. Therefore, the CKF, as well as the adaptive Kalman filter (AKF) will exhibit abnormal behavior and may lead the filter to be suboptimal or even diverge. In this study, the Sage-Husa adaptive Kalman filter (SHAKF) and innovation-based adaptive Kalman filter (IAKF) approaches are employed for adapting the measurement covariance matrix(R). In the case of abrupt changes in the dynamic model, the state covariance matrix (P) is adapted using the strong tracking filter (STF). The performance of these adaptive approaches is evaluated before and after simulating a fault of different sizes in the measurement and dynamic models. The results show that with a large window width, the SHAKF outperforms the CKF and IAKF. However, when the system encounters any fault either in the measurement or dynamic model, the SHAKF loses its optimality and diverges. The sensitivity of the SHAKF to the fault is because the R matrix accumulates with the propagation of the recursive noise estimator. On the other hand, the IAKF and STF provide better performance than both the CKF and SHAKF because the gain matrix is adaptively adjusted to mitigate the influence of the fault, and therefore, they behave normally when a fault of any size occurs in the measurement and/or dynamic model.

Global failure probability function estimation based on an adaptive strategy and combination algorithm

The failure probability function (FPF) expresses the probability of failure as a function of the distribution parameters associated with the random variables . Knowledge on FPF is of much relevance for reliability sensitivity analysis and reliability-based design optimisation. . this paper presents an efficient approach for estimating the FPF based on strategy and . involves three basic elements: (1) a Weighted Importance Sampling approach local FPF estimates; (2) an adaptive strategy of the distribution parameters local FPF estimation; and (3) an optimal combination algorithm, local FPF estimations together to form a global estimate of the FPF. Test and practical examples are presented to demonstrate the efficiency and feasibility of the proposed approach. • A global Adaptive Weigthed Importance Sampling approach for failure probability function estimation is proposed. • Adaptive Weigthed Importance Sampling utilizes weighted importance sampling to efficiently obtain local estimators. • Adaptive Weigthed Importance Sampling includes an adaptive active strategy to guide the local approximations. • Adaptive Weigthed Importance Sampling adopts an optimal combination algorithm to combine the local FPF estimations.

Adaptive estimation for spatially varying coefficient models

<abstract><p>In this paper, a new adaptive estimation approach is proposed for the spatially varying coefficient models with unknown error distribution, unlike geographically weighted regression (GWR) and local linear geographically weighted regression (LL), this method can adapt to different error distributions. A generalized Modal EM algorithm is presented to implement the estimation, and the asymptotic property of the estimator is established. Simulation and real data results show that the gain of the new adaptive method over the GWR and LL estimation is considerable for the error of non-Gaussian distributions.</p></abstract>

A Reliable Online Dictionary Learning Denoising Strategy for Noisy Microseismic Data

Improving the quality of microseismic recordings is a critical step in microseismic data processing. We introduce a wavelet-weighted online dictionary learning (WWODL) denoising strategy for noisy microseismic recordings. We develop an adaptive parameters estimation approach for tunable Q-factor wavelet transform, which provides accurate periodic and non-stationary subband information from microseismic data for online dictionary learning (ODL). A sliding time window is employed to divide the obtained subbands into a series of patches of equal length, which are then assembled into a matrix and fed into the ODL. The subband kurtosis information is weighted to the constraint function of the ODL, further enhancing the sparse coding ability for each subband. Shortening the oscillation duration, an improved ODL is developed with a faster convergence speed in calculating sparse coefficients. Our results confirm that the WWODL can suppress high-frequency, low-frequency, and shared-bandwidth noises and has a minimal impact on the first arrival. The time consumption and signal-to-noise ratio of the WWODL are average 1/8.675 and 56.82% higher than ODL, respectively.

Modeling and Adaptive Parameter Estimation for a Piezoelectric Cantilever Beam

This paper proposes a new adaptive estimation approach to online estimate the model parameters of a piezoelectric cantilever beam. The beam behavior is firstly modeled using partial differential equations (PDE) considering the Kelvin-Voigt damping. To facilitate the estimation of unknown model parameters, the Galerkin’s method is introduced to extract desired vibration modes by separating the time and space variables of the PDE. Then, considering two major vibration modes, the corresponding system model can be represented by a fourth-order ordinary differential equation (ODE). Finally, by using measured input and output information, a novel adaptive parameter estimation strategy is introduced to estimate the unknown parameters of the derived ODE model in real time. For the purpose of driving the parameter updating law, the estimation error is extracted by using an auxiliary variable and a time-varying gain. Consequently, the convergence of the parameter estimation error is rigorously proved based on the Lyapunov theory. Simulations and experimental results show the validity and practicability of the proposed estimation method.

An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range

Capacity estimation is essential for battery health management during the whole lifecycle. The data-driven technique has shown advanced performance in battery capacity estimation. However, the strict limitations on application scenarios and the long duration for feature determination are still the bottlenecks of existing data-driven estimation methods. This study proposes a data-driven capacity estimation method only using 10-min relaxation voltage data, which is adaptable to the high state of charge (SOC) range. The experiments of commercial batteries are designed to investigate the coupling relationship between relaxation voltage, battery aging, and charging cut-off SOC. Further, a novel dual Gaussian process regression (GPR) framework is put forward, in which one GPR is responsible for the battery open-circuit voltage (OCV) estimation using the sequential relaxation voltage feature, and another GPR estimates battery capacity with the corresponding relaxation voltage feature and the estimated OCV. Quantitative experimental results demonstrate that the proposed approach can achieve accurate OCV estimation with extremely sparse voltage data. When SOC is larger than 90%, the capacity estimation achieves a mean absolute error of 2.493% over the battery lifecycle, showing a noticeable improvement over the traditional estimation method.

A statistically homogeneous pixel selection approach for adaptive estimation of multitemporal InSAR covariance matrix

Multitemporal interferometric synthetic aperture radar (InSAR) technology is extensively applied in earth observations. As a critical processing step, the estimation of covariance matrix directly affects the accuracy of its final result. Adaptive multilooking is proven to be an efficient estimator employing statistically homogeneous pixels (SHPs). In this context, the SqueeSAR initially introduced the Kolmogorov-Smirnov test to select SHPs. Furthermore, many test methods were developed based on real-valued data. Therefore, only amplitude is used and complex coherence is typically ignored. In this paper, a new SHP selection algorithm is proposed based on covariance matrix patch, named CMP. In particular, the complex patch is estimated using temporal samples rather than spatial samples. The Kullback-Leibler divergence is employed to evaluate the similarity between two complex patches. Then, the SHP set is initially determined by performing thresholding operation, and an iterative methodology is used to obtain a refined SHP set. Further application of CMP to estimate covariance matrix is investigated. The derived products include the filtered amplitude, interferometric phase and coherence. A series of experiments show the effectiveness of the proposed SHP selection method, especially suitable for cases with only interferometric phase differences. Moreover, due to the high computational cost, this method is generally recommended for small-sized stacks. The experimental results of filtered images also suggest its high performance in both noise suppression and detail preservation.

Adaptive Model Reduction and State Estimation of Agro-hydrological Systems

• An adaptive model reduction approach for agro-hydrological systems. • An adaptive state estimation approach based on the adaptive model reduction. • Extensive simulations with real field data showing the efficiency of the proposed approaches. Closed-loop irrigation can deliver a promising solution for precision irrigation. The accurate soil moisture (state) estimation is critical in implementing a closed-loop agricultural irrigation system. The water dynamics in an agro-hydrological system may be modeled using the Richards equation. Due to the high dimensionality of the Richards equation, it is very challenging to solve an optimization-based advanced state estimator like moving horizon estimation (MHE). This work addresses the aforementioned challenge and proposes a systematic approach for state estimation of large agricultural fields. We first propose a structure-preserving adaptive model reduction method using trajectory-based unsupervised machine learning techniques. The adaptively reduced model is then used in the design of an adaptive MHE algorithm. The performance of the proposed algorithms is first compared with a standard MHE based on a small simulated field. Then, the proposed approach is applied to a large-scale real agricultural field and extensive simulations are carried out to show its effectiveness and applicability.

On-line WSN SoC estimation using Gaussian Process Regression: An Adaptive Machine Learning Approach

Wireless sensor networks (WSN) are low-resource devices that run on small batteries. The availability of battery energy, device drive cycles, and environmental conditions all have an impact on node lifetime. The state of charge (SoC) is an important factor in determining the amount of energy available in the batteries. Accurate SoC estimation is critical for device lifetime prediction and safe device operation. We present a novel approach for adaptive SoC estimation based on Gaussian Process Regression in this paper (GPR). The training data was obtained in a climate-controlled laboratory setting by using IEEE 802.15.4-based drive loads at various temperatures for three different batteries such as Lithium-Ion, Nickel-metal hydride, and Lithium-Polymer. To estimate the SoC, battery parameters such as voltage, capacity, and temperature were directly mapped to the corresponding models. For each battery parameter, the GPR model with hyper tuned Radial Bias Filter (RBF) was trained at temperatures ranging from 5 °C to 45 °C. For model accuracy, the proposed scheme was compared to polynomial regression and support vector machines (SVM). In this regard, the proposed model provided Mean Absolute Error (MAE) values of 2.53 percent, 2.54 percent, and 2 percent, respectively, and Root Mean Square Error (RMSE) values of 0.295, 0.292, and 0.35 for Nickel-metal hydride, Lithium-Polymer, and Lithium-Ion batteries at 25 °C. Our proposed lightweight GPR scheme is, to the best of our knowledge, the only active implementation on embedded platforms for SoC estimation of WSN. Finally, the model was rigorously tested on ARM Cortex M4-based microcontrollers to report real-time online SoC estimation on WSN nodes.