Unscented Kalman Filter Method Research Articles

Optimizing dynamic measurement accuracy for machine tools and industrial robots with unscented Kalman filter and particle swarm optimization methods

The telescoping ballbar is widely utilized for diagnosing accuracy and identifying faults in machine tools and industrial robots. Currently, there are no established standards for determining the optimal feed rate for ballbar tests. This lack of clear guidelines results in time inefficiency in measurements and inconsistencies in dynamic measurements, which complicates the comparison of ballbar test results under various conditions or across different machine platforms. To mitigate dynamic variations in ballbar results, an updated ballbar data processing method that integrates the unscented Kalman filter (UKF) and particle swarm optimization (PSO) was developed and validated using real ballbar data measured at multiple feed rates and simulated data with varying vibration magnitudes generated through the Renishaw ballbar simulator. Experimental results revealed that the dynamic components extracted from the ballbar results were observed to increase in correlation with the vibration measured at different feed rates and from the simulations. Moreover, the variations in the results measured at different feed rates after PSO-UKF processing were significantly reduced. The findings confirm the effectiveness of the proposed method in minimizing the dynamics of the ballbar results. Ultimately, this approach enhances the efficiency and accuracy of ballbar testing and offers a general method for improved diagnostics.

A data and physical model joint driven method for lithium-ion battery remaining useful life prediction under complex dynamic conditions

Accurate remaining useful life (RUL) prediction of batteries plays an important role in battery management. The existing methods mainly rely on the battery test data under ideal operating conditions to show good performance. However, the actual operating conditions of batteries are usually complex, which bring great challenges to the RUL prediction. To improve the accuracy and the generalization ability of RUL prediction method, a fusion method of electrochemical–thermal model (ECT) and unscented kalman filter (UKF) is proposed for lithium-ion battery. Firstly, an ECT–based capacity degradation model is established by coupling pseudo two dimensions (P2D) electrochemistry model, 3D thermal model and solid electrolyte interface (SEI) formation model. Secondly, the UKF method is used to update iteratively the parameters of model to improve its accuracy. Finally, several sets of operating data of lithium-ion battery under different conditions are used for RUL prediction, followed by the comparative analysis of different models and algorithms. As a result, the proposed fusion method not only exhibits better accuracy in RUL prediction under ideal conditions, but also shows excellent accuracy and generalization for other dynamic stochastic operating conditions.

Vehicle state and parameter estimation based on adaptive anti-outlier unscented Kalman filter and GA-BPNN method

A multi-machine-learning improved adaptive Kalman filtering method is proposed to address the problem of handling abnormal data encountered in the vehicle state estimation. Firstly, the unscented Kalman filter (UKF) algorithm is improved by introducing a BP neural network improved by the genetic algorithm (GA-BPNN) to regulate and correct the global error of the UKF method. Then, the anti-outlier technique is applied to fully eliminate isolated and speckled outliers in the measurement, achieving further improvement on GA-BPNN-UKF and significantly improving the robustness of the filtering process. Finally, a simulation is applied to verify the effectiveness of the proposed new algorithm, and then its results are analyzed to obtain a firm substantiation of its effectiveness for further practical applications. The simulation results indicate that the estimation performance of the GA-BPNN algorithm is significantly better than that of Extended Kalman filter (EKF) method.

Synchronous identification of nonlinear structural parameters and unknown external excitation based on improved UKF

The traditional Unscented Kalman Filter (UKF) approach for nonlinear structural identification usually requires knowledge of external excitation. However, in real application, obtaining the input excitation of nonlinear structures can be challenging due to measurement error or difficulty in acquiring the accurate information. Consequently, the real-time application of the UKF method will be constrained. To address this issue, this paper develops an improved UKF-based synchronous identification method of nonlinear structural parameters and unknown external excitation. The proposed method starts with a preliminary estimate of the unknown excitation using the current predicted values of structural dynamic responses and parameters. Subsequently, the external excitation is further identified based on the updated state vector. In addition, to reduce the effect of measurement noise on the nonlinear structural identification and ensure the accuracy of the identified results, a Kalman Filter (KF) process is embedded in the UKF to optimize measurement noise covariance matrix in real time. The practicability and accuracy of the improved UKF method are confirmed by conducting numerical simulations on a single degree-of-freedom (S-DOF) and a six degree-of-freedom (six-DOF) nonlinear structural systems due to unknown seismic excitation. Moreover, a shake table test of a five-storey steel frame subjected to seismic excitation is employed to attest the feasibility of the proposed method. Both numerical and experimental results demonstrate that the improved UKF method can effectively achieve synchronous identification for nonlinear structural parameters and unknown excitation.

Degradation prediction of proton exchange membrane fuel cells with model uncertainty quantification

This study presents a new approach for state of health (SOH) estimation and degradation trend prediction of proton exchange membrane fuel cells (PEMFCs) considering model uncertainty quantification. The model uncertainty is considered as completely unknown in this paper, and the Bayesian framework-based model uncertainty and state simultaneous estimation (Bayes-MUSE) method is proposed to quantify the model uncertainty from the perspective of the form of its effect on the system state transfer, and this method of quantifying model uncertainty is first applied to PEMFC long-term aging prediction and RUL estimation. The proposed Bayes-MUSE method is compared with extended Kalman filter (EKF), Unscented Kalman filter (UKF) and Unscented Particle filter (UPF) methods in terms of SOH estimation, noise immunity, aging prediction performance and RUL estimation using two aging test datasets, and the method is verified to have excellent noise immunity and the highest long-term aging prediction and RUL estimation accuracy. Compared with EKF, UKF and UPF methods, the proposed Bayes-MUSE method has a maximum improvement of 93.95% and 52.2% in RUL estimation accuracy of the two data sets under quasi-dynamic and static conditions, respectively.

Analysis of GNSS and IMU Sensor Data Fusion Using the Unscented Kalman Filter Method on Medical Drones in Open Air

Accurate and efficient position estimation is an important element in flight missions for autonomous aerial vehicles such as Unmanned Aerial Vehicles (UAVs). UAVs rely on sensors that provide information regarding position, speed, and orientation. GPS and IMU are sensors that can provide navigation information, where each sensor has characteristics that can complement each other’s shortcomings. Research related to the integration of GPS and IMU sensors for positioning with increasingly higher accuracy has been widely applied, one of which is the Kalman Filter method. Kalman filter not only performs digital signal filtering but is also capable of smoothing and predicting recursion to obtain the most accurate estimation results. Along with the development of the modification of the Kalman Filter, the Unscented Kalman Filter (UKF) method is a solution in overcoming nonlinear systems with estimation results approaching the actual value, so that the positioning accuracy is higher and convergent. This study intends to analyze the fusion performance of GPS and IMU sensor data using the UKF method on Medical Drones in an open space in order to obtain a position with high accuracy. UKF simulations on Medical Drones were carried out using a mathematical model on a 6 DOF (Degree of Freedom) UAV. The sensor data fusion performance was tested by comparing the results of the UKF method with the EKF on the Geopointer software. The results showed that the UKF simulation gave a positional accuracy of 0.022 m to the measurement data. The UKF simulation results in a better positioning accuracy when compared to EKF processing on the Geopointer software which only achieves a position accuracy of 8.467 m. The low accuracy of the EKF processing results is due to the mismatch of the IMU parameters obtained with the actual Medical Drone parameters.

A deep reinforcement learning approach for state of charge and state of health estimation in lithium-ion batteries

Accurate estimation of state variables such as State of Charge (SOC) and State of Health (SOH) is pivotal in the management of lithium-ion batteries. Existing methods, including the unscented Kalman filter (UKF), often require manual tuning of parameters and may not adapt well to the non-linear and non-stationary characteristics of batteries. This paper introduces a novel approach to optimize the parameters of an adaptive unscented Kalman filter (AUKF) using deep reinforcement learning (DRL). The DRL agent learns to adjust the parameters of the AUKF to maximize the estimation accuracy through interaction with the battery environment. This approach is capable of adapting to different battery types and operating conditions, eliminating the need for manual parameter tuning. Our results indicate that the DRL-optimized AUKF outperforms traditional UKF methods in terms of SOC and SOH estimation accuracy, demonstrating the potential of this approach for improving battery management systems.

A Novel Adaptive Square Root UKF with Forgetting Factor for the Time-Variant Parameter Identification

The unscented Kalman filter (UKF) serves as an efficient estimator widely utilized for the recursive identification of parameters. However, the UKF is not well suited for tracking time-variant parameters. Moreover, the unscented transformation (UT) used in the UKF typically relies on Cholesky decomposition to perform the square root operation of the covariance matrix. This method necessitates the matrix to maintain symmetry and positive definiteness. Due to the adverse influence of rounding error and noise, it becomes challenging to guarantee the positive definiteness of the matrix in each recursive step for practical engineering. The square root UKF (SRUKF) eliminates the need for the square root operation in the UT by directly updating the square root of the covariance matrix during each recursion. However, the SRUKF still relies on the rank 1 update to the Cholesky factorization to perform the recursive process, which also necessitates the matrix to be positive definite. Furthermore, the SRUKF is ineffective in the identification of time-variant parameters. Therefore, this paper proposes a modification to the SRUKF that ensures unconditional numerical stability by utilizing QR decomposition. Subsequently, the modified square root UKF (MSRUKF) method is enhanced by incorporating an adaptive forgetting factor that can be adjusted based on the residual information from each recursive step. This adaptation leads to the development of the adaptive SRUKF with forgetting factor (ASRUKF-FF) method, which significantly improves the tracking capability for time-variant parameters. To validate the effectiveness of the proposed method, this paper demonstrates its application in identifying the time-variant stiffness and damping parameters of a three-story frame structure. In addition, the method is employed to estimate the time-variant stiffness of the bridge excited by vehicles. The simulation results show that the proposed method has the superiority of high accuracy, strong robustness, and widespread applicability, even with incomplete measurements and inappropriate parameter settings.

A fuzzy robust two-stage unscented Kalman filter method for uncertainty and state of charge estimation of lithium-ion batteries

For the practical state of charge (SoC) estimation of lithium-ion batteries, the unknown battery model uncertainties should be considered. Conventionally, the robust SoC estimation method uses a bounded matrix to model battery uncertainties. However, since the upper bound of the uncertainty is unknown, the robust estimation would be conservative and inaccurate. To accurately estimate the SoC of lithium-ion batteries, this paper proposes a novel fuzzy robust two-stage unscented Kalman filter (FRTSUKF) method to model the battery uncertainty with unknown statistical characteristics. The proposed estimator is able to estimate the model uncertainties and does not require the statistical characteristics of the uncertainties. Using the estimated uncertainties, the SoC estimation is corrected, and the destructive effect of the uncertainties on the estimation accuracy is eliminated correctly. To make the estimation more accurate and practical during experiments, the covariance matrix of the measurement noise is recursively adjusted using a fuzzy system. In addition, as the battery aging increases the model uncertainty, the proposed observer is able to adjust the SoC estimation at each battery life period using the estimated uncertainties. In the first stage, the state variables of the battery model are estimated. The covariance matrix of the measurement noise is recursively adjusted using a fuzzy system, which makes the estimation more accurate and practical during experiments. In the second stage, the unknown model uncertainties are estimated. Based on the estimated uncertainties, state variables, including SoC, are adjusted to compensate for the adverse effects of the model inaccuracy on the state estimation. The experimental results show that in addition to the ability for the battery model uncertainty estimation, the proposed estimator is more accurate, as much as 1 % and more robust than other Kalman-based methods for aged batteries with more uncertain models.

Attitude filtering with uncertain process and measurement noise covariance using SVD‐aided adaptive UKF

AbstractIt is presented in this article how to simultaneously alter the process and measurement noise covariance matrices for nontraditional attitude filtering technique. The unscented Kalman filter (UKF) and singular value decomposition (SVD) methods are integrated in the nontraditional attitude filtering algorithm to estimate a nanosatellite's attitude with an inherent robustness feature. The SVD approach determines the attitude of the nanosatellite and provides one estimate at a single frame utilizing measurements from the magnetometer and Sun sensor as the initial stage of the algorithm. These attitude terms are subsequently fed into the UKF with their error covariances, which makes the filter robust inherently. The attitude estimations of the satellite are compared between the filters presented. The Q (process noise covariance) adaption approach with multiple scale factors is specifically suggested for differences in between the process channels. Performance of the multiple scale factors‐based adaptive SVD‐aided UKF is examined in the event of process noise increase, which may result from changes in the environment or satellite dynamics.

SOC estimation of lithium battery based on multi-innovation unscented Kalman filter algorithm

Precise of the state is an imperative necessity for ensuring the dependable operation of lithium-ion batteries. The state of charge (SOC) of lithium-ion batteries is arduous to determine precisely. Therefore, a novel method was proposed, which incorporates the Unscented Kalman Filter (UKF) algorithm in combination with the theory of multi-innovation. This method exhibits an enhanced estimation precision of the UKF algorithm by means of re-utilizing prior information. The process encompasses a charge-discharge experiment, and identification of offline parameters for obtaining the RC equivalent-circuit model parameter. The model was verified to be accurate and correct through simulation using Matlab/Simulink. The UKF and MIUKF methods are utilized for estimating the actual operational state of a single lithium battery. According to experimental results, MIUKF offers a higher degree of accuracy and effectiveness in estimating the SOC than the UKF algorithm, with a smaller margin of SOC estimation error.

Key Technologies of Collaborative Target Detection between Self-propelled Anti-aircraft Weapons

In the case of a lack of target information, a self-propelled anti-aircraft weapon can complete its own combat functions by using the detection information of other vehicles through detection coordination technology. When using the detection coordination technology to process the detection information of other vehicles into information that can be used by the vehicle, the relative position and attitude of the vehicle and data transmission delay will affect the accuracy of the processed target information. With the system of detection collaborative technology expounded, emphasis is attached to the research on the detection coordination method based on pure angles without considering distance. A mathematical model of detection coordination technology is created, the approximate statistical law of the processed detection coordination information is obtained through the Monte Carlo method, and the influence of various factors on transmission error analyzed. In addition, the polynomial curve fitting method is used for time alignment, and the unscented Kalman filter method based on singular value decomposition adopted to reduce the information transfer error. The simulation results show the the method is effective for that end.

Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network

Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network

Rapid Detection and Orbital Parameters’ Determination for Fast-Approaching Non-Cooperative Target to the Space Station Based on Fly-around Nano-Satellite

Non-cooperative targets, such as space debris, defunct spacecrafts and LEO constellation satellites, have brought serious risks to the space station. The rapid detection and orbital parameters’ determination of the fast-approaching non-cooperative target can greatly improve the protection ability of the space station. In this paper, a novel rapid detection and orbital parameters’ determination method based on the collaborative observation of the space station, and a fly-around nano-satellite is developed. The early-warning region of the space station is established, and considering the observation constraints of the nano-satellite, the non-cooperative target detection strategy is provided, which includes a collaborative observation configuration as well as the attitude variation of the cameras, and the detection efficiency is analyzed. Then, the orbital parameters’ filtering model of the non-cooperative target based on the collaborative observation is constructed, and the Unscented Kalman filter method is utilized to determinate the orbital parameters of the non-cooperative target. Considered the observability of the initial collaborative observation configuration, this paper analyzes the observation configuration with low observability in different scenarios, and proposes an optimal orbital maneuver algorithm for the nano-satellite. This algorithm can realize a fuel-optimal orbital maneuver that satisfies the minimum line-of-sight angle constraint of the collaborative observation.

Application of the Unscented Kalman Filter to Estimate the Material Properties of a Layered Half-Space

Full waveform inversion (FWI) to estimate physical properties of a system is one of the major research topics in science and engineering. This study proposes a probabilistic approach toward these solutions by applying the unscented Kalman filter (UKF). The responses of the vertical displacements of a layered half-space subjected to a harmonic vertical disk load on the surface are calculated from an estimated profile of shear-wave velocities and compared with measurements or observations. In the calculation of the dynamic responses, the thin-layer method (TLM), which is efficient for layered media, is employed. In order to improve the solutions to the considered inverse problem, regularization terms are included in the observations so that the differences in the material properties between two consecutive layers vanish. The proposed UKF method is demonstrated with a variety of FWI problems in a layered half-space. The results show that the proposed method can estimate the material properties of a layered half-space accurately.

An unscented kalman filtering method for estimation of state-of-charge of lithium-ion battery

Accurate estimation of battery state of charge (SOC) is of great significance to improve battery management and service life. An unscented Kalman filter (UKF) method is used to increase the accuracy of SOC estimation in this paper. Firstly, a battery model that the parameters are identified by using the least squares algorithm is established, which is foundation of the two-order RC equivalent circuit model. Secondly, SOC is estimated by UKF. In order to validate the method, experiments have been carried out under different operating conditions for LiFePO4 batteries. The obtained results are compared with that of the extended Kalman filter. Finally, the comparison shows that the UKF method provides better accuracy in the battery SOC estimation. Its estimation error is less than 2%, which is better than EKF algorithm. An effective method is provided for state estimation for battery management system.

A fusion estimation of the peak tire–road friction coefficient based on road images and dynamic information

To accurately acquire the peak tire–road friction coefficient, a fusion estimation framework combining vision and vehicle dynamic information is established. First, information for the road ahead is collected in advance from an image captured by a camera, and the road type with its typical range of tire–road friction coefficients is identified with a lightweight convolutional neural network. Then, an unscented Kalman filter (UKF) method is established to estimate the tire–road friction coefficient value directly according to the dynamic vehicle states. Next, the results from the road-type recognition and dynamic estimation methods are spatiotemporally synchronized. Finally, a confidence-based vision and vehicle dynamic fusion strategy is proposed to obtain an accurate peak tire–road friction coefficient. The virtual and real vehicle test results suggest that the proposed fusion estimation strategy can accurately determine the peak tire–road friction coefficient. The proposed strategy can more precisely acquire the tire–road friction coefficient than can the general vision-based estimation method and is superior to the dynamic-based estimation method in that it eliminates the need for sufficient tire excitation to some extent.

Research on Mobile Terminal Mobility Prediction Method Based on Unscented Kalman Filter

For mobile terminal users in the process of moving, due to the uncertainty of movement, how to effectively predict and reasonably allocate the Edge Servers (ESs) for mobile terminal users has become the key to solving the problem. Therefore, this paper proposes and designs a mobile terminal user mobility prediction method based on an Unscented Kalman Filter (UKF). According to the application scenario of Mobile Edge Computing (MEC), the edge server with computing resources is placed at the edge node of the network. The state equation and observation equation are constructed through the trajectory points of the mobile terminal users, and the trajectory prediction is completed using the UKF. By comparing the No Filter. Method, the Linear Kalman Filter. Method and the UKF method, the research results show that the proposed method can effectively reduce the error and improve the success rate of the task compared with the other two methods so that the prediction trajectory of mobile terminal users is more accurate and more applicable.

Unscented Kalman Filter and H-Infinity for Travel Company Stock Price Estimation

The travel and hotel industry is one of the industries experiencing rapid growth. As the population grows, the need for travel and accommodation services gets higher. This is one of the factors contributing a rapid increase in such service industry. Competition in the economy and business world is getting tougher from year to year both within a country and abroad. Considering that Indonesia is a country comprised of many islands with a variety of natural beauty, it has the very potential for tourist resort attraction. This kind of thing leads to the growth of the Hotel and Travel industry to support tourism development. With such rapid service industry development, supported by promising business opportunities, investors for such sector are encouraged. The right way to reduce risk for investors interested is to develop a system for estimating the stock prices. Therefore, in this study, the stock price estimation method applied for travel companies adopted Advanced Kalman Filter, a comparison of H-Infinify and Unscented Kalman Filter (UKF) as a chart for investors to take into consideration in their investment decision making. The simulation results showed that the UKF method had higher accuracy than the H-Infinify method with an error by the UKF of 3.2% and that by the H-Infinify of 9.6%.

A BP neural network-Ant Lion Optimizer and UKF method for SOC estimation of lithium-ion batteries

Accurate state of charge (SOC) estimation is of great significance to promote the development of new energy vehicles. And a battery model’s accuracy is of great significance for the battery SOC estimation accuracy. To this end, a method based on back propagation neural network-Ant Lion Optimizer (BPNN-ALO) and unscented Kalman filter (UKF) is proposed. First, a 2-RC battery model is established and BPNN is used to fit the OCV-SOC corresponding relationship. Second, the BPNN and ALO are combined to complete the battery model’s parameter identification, and UKF is used to complete SOC estimation. Finally, verify the BPNN-ALO-UKF under two working conditions, and compare it with the other two methods. The test results show that the proposed method has higher SOC estimation accuracy, the minimum values of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are only 0.51% and 0.40%, respectively. And under different working conditions, it also has a better generalization and robustness.