Adaptive Cubature Kalman Filter Research Articles

State of energy estimation of lithium-ion battery based on long short-term memory optimization Adaptive Cubature Kalman filter.

State of energy (SOE) is an important parameter to ensure the safety and reliability of lithium-ion battery (LIB) system. The safety of LIBs, the development of artificial intelligence, and the increase in computing power have provided possibilities for big data computing. This article studies SOE estimation problem of LIBs, aiming to improve the accuracy and adaptability of the estimation. Firstly, in the SOE estimation process, adaptive correction is performed by iteratively updating the observation noise equation and process noise equation of the Adaptive Cubature Kalman Filter (ACKF) to enhance the adaptive capability. Meanwhile, the adoption of high-order equivalent models further improves the accuracy and adaptive ability of SOE estimation. Secondly, Long Short-term Memory (LSTM) is introduced to optimize Ohmic internal resistance (OIR) and actual energy (AE), further improving the accuracy of SOE estimation. Once again, in the process of OIR and AE estimation, the iterative updating of the observation noise equation and process noise equation of ACKF were also adopted to perform adaptive correction and enhance the adaptive ability. Finally, this article establishes a SOE estimation method based on LSTM optimized ACKF. Validate the LSTM optimized ACKF method through simulation experiments and compare it with individual ACKF methods. The results show that the ACKF estimation method based on LSTM optimization has an SOE estimation error of less than 0.90% for LIB, regardless of the SOE at 100%, 65%, and 30%, which is more accurate than the SOE estimation error of ACKF alone. It can be seen that this study has improved the accuracy and adaptability of LIB's SOE estimation, providing more accurate data support for ensuring the safety and reliability of lithium batteries.

Adaptive Cubature Kalman Filter for Inertial/Geomagnetic Integrated Navigation System Based on Long Short-Term Memory Network

Inertial navigation systems experience error accumulation over time, leading to the use of integrated navigation as a classical solution to mitigate inertial drift. This provides a novel approach to navigation and positioning by using the combined advantages of inertial and geomagnetic navigation systems. However, inertial/geomagnetic navigation is affected by significant magnetic interference in practical scenarios, resulting in reduced navigation accuracy. This research introduces a new neural network-assisted integrated inertial–geomagnetic navigation method (IM-NN), and utilizes the adaptive cubature Kalman filter to integrate attitude information from geomagnetism and inertial sensors. A model was created utilizing a Long Short-Term Memory Network (LSTM) to represent the relationship between specific force, angular velocity, and integrated navigation attitude information. The dynamics were estimated based on current and previous Inertial Measurement Unit (IMU) data using IM-NN. This study demonstrated that the method effectively corrected inertial accumulation errors and mitigated geomagnetic disruption, resulting in a more accurate and dependable navigation solution in environments with geomagnetic rejection compared to conventional single inertial navigation methods.

Adaptive Cubature Kalman Filter for Inertial/Geomagnetic Integrated Navigation System Based on Long Short-Term Memory Network

Inertial navigation systems experience error accumulation over time, leading to the use of integrated navigation as a classical solution to mitigate inertial drift. This provides a novel approach to navigation and positioning by using the combined advantages of inertial and geomagnetic navigation systems. However, inertial/geomagnetic navigation is affected by significant magnetic interference in practical scenarios, resulting in reduced navigation accuracy. This research introduces a new neural network-assisted integrated inertial–geomagnetic navigation method (IM-NN), and utilizes the adaptive cubature Kalman filter to integrate attitude information from geomagnetism and inertial sensors. A model was created utilizing a Long Short-Term Memory Network (LSTM) to represent the relationship between specific force, angular velocity, and integrated navigation attitude information. The dynamics were estimated based on current and previous Inertial Measurement Unit (IMU) data using IM-NN. This study demonstrated that the method effectively corrected inertial accumulation errors and mitigated geomagnetic disruption, resulting in a more accurate and dependable navigation solution in environments with geomagnetic rejection compared to conventional single inertial navigation methods.

An Adaptive Cubature Kalman Filter Based on Resampling-Free Sigma-Point Update Framework and Improved Empirical Mode Decomposition for INS/CNS Navigation

For the degradation of the filtering performance of the INS/CNS navigation system under measurement noise uncertainty, an adaptive cubature Kalman filter (CKF) is proposed based on improved empirical mode decomposition (EMD) and a resampling-free sigma-point update framework (RSUF). The proposed algorithm innovatively integrates improved EMD and RSUF into CKF to estimate measurement noise covariance in real-time. Specifically, the improved EMD is used to reconstruct measurement noise, and the exponential decay weighting method is introduced to emphasize the use of new measurement noise while gradually discarding older data to estimate the measurement noise covariance. The estimated measurement noise covariance is then imported into RSUF to directly construct the posterior cubature points without a resampling step. Since the measurement noise covariance is updated in real-time and the prediction cubature points error is directly transformed to the posterior cubature points error, the proposed algorithm is less sensitive to the measurement noise uncertainty. The proposed algorithm is verified by simulations conducted on the INS/CNS-integrated navigation system. The experimental results indicate that the proposed algorithm achieves better performance for attitude angle.

A Fusion Adaptive Cubature Kalman Filter Approach for False Data Injection Attack Detection of DC Microgrids

With the widespread application of information technology in microgrids, microgrids are evolving into a class of power cyber–physical systems (CPSs) that are deeply integrated with physical and information systems. Due to the high dependence of microgrids’ distributed cooperative control on real-time communication and system state information, they are increasingly susceptible to false data injection attacks (FDIAs). To deal with this issue, in this paper, a novel false data injection attack detection method for direct-current microgrids (DC MGs) was proposed, based on fusion adaptive cubature Kalman filter (FACKF) approach. Firstly, a DC MG model with false data injection attack is established, and the system under attack is analyzed. Subsequently, an FACKF approach is proposed to detect attacks, capable of accurately identifying the attacks on the DC MG and determining the measurement units injected with false data. Finally, simulation validations were conducted under various DC MG model conditions. The extensive simulation results demonstrate that the proposed method surpasses traditional CKF detection methods in accuracy and effectiveness across different conditions.

Sideslip Angle Fusion Estimation Method of Three-Axis Autonomous Vehicle Based on Composite Model and Adaptive Cubature Kalman Filter

A sideslip angle fusion estimation strategy of three-axis vehicle based on adaptive cubature Kalman filter is investigated in this paper. According to the dynamics model, kinematics model of the three-axis vehicle, and considering the influence of tire nonlinearity, the vehicle state estimators under different conditions are designed by using the adaptive cubature Kalman filter algorithm. The dynamic-model-based estimator with linear tire model, dynamic-model-based estimator with nonlinear tire model, and kinematical-model-based estimator are proposed, then, according to the application characteristics of different estimators, a fusion estimation strategy of vehicle sideslip angle based on adaptive fuzzy weight controllers is designed, so as to improve the overall estimation accuracy by integrating the advantages of the three estimators. The simulation and experimental results show that the presented fusion estimation strategy can effectively improve the estimation accuracy of vehicle sideslip angle, and the comprehensive estimation accuracy reaches 94.37%.

A novel adaptive maximum correntropy cubature Kalman filter based on multiple fading factors

In this paper, an adaptive maximum correntropy cubature Kalman filter based on multiple fading factors (MAMCKF) is proposed to address the problem of inaccurate process noise covariance and unknown measurement noise covariance together with outliers in target tracking. Although there are many adaptive filters and robust filters have been proposed to handle unknown measurement noise covariance or measurement outliers, most filters cannot deal with both unknown noise covariance and outliers simultaneously. In this article, we propose an adaptive and robust cubature Kalman filter. The modified measurement noise covariance matrix (MNCM) and innovation covariance matrix are used to construct multiple fading factors for correcting the prediction error covariance matrix (PECM), which can achieve adaptability. Then, the maximum correntropy criterion (MCC) is introduced to suppress outliers, which further enhances the robustness. Compared with the existing approaches, the proposed approach improves the performance by at least 5% in unknown time-varying noise, unknown time-varying heavy-tailed noise, and non-Gaussian heavy-tailed noise scenarios. The simulation results show that the proposed approach can effectively suppress inaccurate process noise covariance and unknown time-varying measurement noise together with outliers. Compared with the existing filtering approaches, the proposed approach exhibits both adaptability and robustness.

Enhanced dynamic state estimation of regional new energy power system under different abnormal scenarios

AbstractThe high proportion of renewable energy sources in the power grid increases the failure probability of the system, which becomes a new challenge for the safe and stable operation of the regional power grid. To ensure stable control of the power network with substantial renewable energy integration, this article proposes a new method that combines the long short‐term memory (LSTM) neural networks and adaptive cubature Kalman filter (ACKF) to improve the prediction accuracy of mutation data inherited from the renewable generation. Four abnormal scenarios, including low voltage ride‐through (LVRT), high voltage ride‐through (HVRT), continuous fault ride‐through and bad data injection of the regional power grid are investigated through extensive case studies. The proposed method is implemented on the IEEE 30‐node system for performance verification. The simulation results demonstrate that the proposed method has considerably higher robustness than the traditional Kalman filter algorithm and can effectively improve the overall state estimation accuracy of the renewable energy power system under different scenarios.

State of energy estimation of lithium-ion battery based on long short-term memory optimization Adaptive Cubature Kalman filter.

State of energy (SOE) is an important parameter to ensure the safety and reliability of lithium-ion battery (LIB) system. The safety of LIBs, the development of artificial intelligence, and the increase in computing power have provided possibilities for big data computing. This article studies SOE estimation problem of LIBs, aiming to improve the accuracy and adaptability of the estimation. Firstly, in the SOE estimation process, adaptive correction is performed by iteratively updating the observation noise equation and process noise equation of the Adaptive Cubature Kalman Filter (ACKF) to enhance the adaptive capability. Meanwhile, the adoption of high-order equivalent models further improves the accuracy and adaptive ability of SOE estimation. Secondly, Long Short-term Memory (LSTM) is introduced to optimize Ohmic internal resistance (OIR) and actual energy (AE), further improving the accuracy of SOE estimation. Once again, in the process of OIR and AE estimation, the iterative updating of the observation noise equation and process noise equation of ACKF were also adopted to perform adaptive correction and enhance the adaptive ability. Finally, this article establishes a SOE estimation method based on LSTM optimized ACKF. Validate the LSTM optimized ACKF method through simulation experiments and compare it with individual ACKF methods. The results show that the ACKF estimation method based on LSTM optimization has an SOE estimation error of less than 0.90% for LIB, regardless of the SOE at 100%, 65%, and 30%, which is more accurate than the SOE estimation error of ACKF alone. It can be seen that this study has improved the accuracy and adaptability of LIB's SOE estimation, providing more accurate data support for ensuring the safety and reliability of lithium batteries.

Robust State Estimation Using the Maximum Correntropy Cubature Kalman Filter with Adaptive Cauchy-Kernel Size

The maximum correntropy criterion (MCC), as an effective method for dealing with anomalous measurement noise, is widely applied in the design of filters. However, its performance largely depends on the proper setting of the kernel bandwidth, and currently, there is no efficient adaptive kernel adjustment mechanism. To deal with this issue, a new adaptive Cauchy-kernel maximum correntropy cubature Kalman filter (ACKMC-CKF) is proposed. This algorithm constructs adaptive factors for each dimension of the measurement system and establishes an entropy matrix with adaptive kernel sizes, enabling targeted handling of specific anomalies. Through simulation experiments in target tracking, the performance of the proposed algorithm was comprehensively validated. The results show that the ACKMC-CKF, through its flexible kernel adaptive mechanism, can effectively handle various types of anomalies. Not only does the algorithm demonstrate excellent reliability, but it also has low sensitivity to parameter settings, making it more broadly applicable in a variety of practical application scenarios.

Soc Estimation of Li-ion Battery Based on Adaptive CKF Algorithm

A second-or der RC equivalent circuit model was established to improve the estimation accuracy of state of charge (SOC) of power Li-ion batteries, and the model parameters were identified by the recursive least square method with forgetting factor (FFRLS). On this basis, an adaptive cubature kalman filter (ACKF) algorithm was proposed to adaptively modify the process noise covariance matrix and the measurement noise covariance matrix to improve the SOC estimation accuracy. Finally, the SOC estimation algorithm was verified by MATLAB simulations. The results show that compared with UKF and CKF algorithms, the proposed algorithm has higher estimation accuracy and robustness, and can meet the application requirements.

An improved adaptive square root cubature Kalman filter method for estimating state-of-charge of lithium-ion batteries

An improved adaptive square root cubature Kalman filter method for estimating state-of-charge of lithium-ion batteries

State of Charge Estimation of Lithium-Ion Batteries Based on Vector Forgetting Factor Recursive Least Square and Improved Adaptive Cubature Kalman Filter

Accurate online parameter identification and state of charge (SOC) estimation are both very crucial for ensuring the operating safety of lithium-ion batteries and usually the former is a base of the latter. To achieve accurate and stable SOC estimation results, this paper proposes a model-based method, which incorporates a vector forgetting factor least square (VFFLS) algorithm and an improved adaptive cubature Kalman filter (IACKF). Firstly, considering it is difficult for the traditional forgetting factor recursive least square (FFRLS) algorithm to balance the accuracy, convergence, and stability for multiple parameters with different time-varying periods, an improved VFFLS method is employed to determine the multiple parameters of the first-order RC battery model online. It supersedes the single forgetting factor in the FFRLS with multiple forgetting factors in a vector form for improving adaptive capability to multiple time-varying parameters. Secondly, aiming at the fact that the standard cubature Kalman filter (CKF) cannot operate properly when the error covariance matrix is non-positive definite, which is caused by disturbance, initial error, and the limit of the computer word length, the UR decomposition rather than the Cholesky decomposition is applied, thus improving the algorithm stability. In addition, an adaptive update strategy is added to the CKF to enhance accuracy and convergence speed. Finally, comparative experiments with different operating patterns, positive and non-positive definite error covariance matrices, and temperatures are carried out. Experimental results showed that the proposed method can estimate the SOC accurately and stably.

Hydrostatic pressure adaptive dual-polarized model for state of charge estimation of lithium-ion batteries

Pressure-tolerated lithium-ion batteries have been used in autonomous underwater vehicle (AUV), due to their ability to withstand pressure directly and save structural parts that protect itself. As an important component of the battery, state of charge (SOC) is critical for evaluating the reliable operation of AUV batteries. The influence of hydrostatic pressure is not considered in typical SOC estimation process, which leads to a large deviation of terminal voltage estimation, and finally the accurate estimation of SOC cannot be realized. In this paper, a dual-polarized equivalent circuit model dependent on hydrostatic pressure is established, which fully considers the influence of pressure on the internal characteristic parameters of the battery. The characteristic parameters in the equivalent circuit model are measured at hydrostatic pressure and identified by forgetting factor recursive least squares (FFRLS). Compared with the blank model without considering hydrostatic pressure, the proposed model can capture voltage and SOC, which can contribute to the estimation accuracy of terminal voltage in a wide pressure range. The typical algorithms of unscented Kalman filter (UKF), cubature Kalman filter (CKF) and adaptive cubature Kalman filter (ACKF) are used to estimate the SOC based on the two models (OCV-SOC-Pressure and OCV-SOC) at different hydrostatic pressures. The results show that the accuracy of the SOC estimation algorithm under different hydrostatic pressure environments is improved, which is attributed to the compensation correction of the original OCV-SOC model. The root mean square error (RMSE) value of the ACKF algorithm based on the hydrostatic pressure adaptive multivariate regression model is 0.94 %, which significantly improves the SOC estimation effectiveness of the OCV-SOC model without considering the pressure factor (RMSE is 7.36 %) at the hydrostatic pressure of 60 MPa. At the preset different SOC initial values, the three algorithms based on the hydrostatic pressure adaptive multivariate regression model can achieve fast convergence, and ACKF shows the best environmental adaptability.

Development of a GNSS/INS-based automatic navigation land levelling system

In this study, an automatic navigation land levelling system based on the global navigation satellite system (GNSS) and an inertial navigation system (INS) was developed to improve the efficiency and accuracy of land levelling. The developed system platform was composed of an integrated positioning unit, steering and lifting controller, and vehicle control terminal. Automatic navigation land levelling vehicle terminal software was designed to realize data communication, processing and storage, and display. Through the cooperation of the platform and software, the system completed the automatic navigation terrain survey, automatic planning/design and automatic navigation land levelling functions. In addition, a GNSS/INS integrated positioning method based on an adaptive square root cubature Kalman filter (ASRCKF) was designed, which obtained the correction positioning points of the tractor and scraper and adaptively predicted the state and measurement noise of the SRCKF. An automatic steering and lifting control method was designed to realize synchronous automatic navigation land levelling control, which was combined with the positioning method. Field experiments were carried out to validate the path tracking effect of the dynamic filtering navigation and a comparison of the manual and automatic land levelling operation performance. The navigation experimental results showed that the average root mean square error (RMSE) of the tractor and the scraper were 2.94 cm and 3.15 cm in tracking a straight path and 8.61 cm and 9.83 cm in tracking a curved path, respectively. The average RMSE of the path tracking based on the ASRCKF positioning method was reduced by 75.2% and 67.4% for the tractor and the scraper, respectively. The land levelling experimental results showed that the elevation distribution around the datum elevation of the automatic navigation land levelling increased by 8.7% compared to the manual operation. The GNSS/INS-based automatic navigation land levelling system can notably reduce the lateral position deviations, improve the control accuracy and land levelling efficiency, and meet the control accuracy requirements of unmanned tractor-scraper land levelling operations.

A Novel Adaptive Robust Cubature Kalman Filter for Maneuvering Target Tracking with Model Uncertainty and Abnormal Measurement Noises.

The features of measurement and process noise are directly related to the optimal performance of the cubature Kalman filter. The maneuvering target model's high level of uncertainty and non-Gaussian mean noise are typical issues that the radar tracking system must deal with, making it impossible to obtain the appropriate estimation. How to strike a compromise between high robustness and estimation accuracy while designing filters has always been challenging. The H-infinity filter is a widely used robust algorithm. Based on the H-infinity cubature Kalman filter (HCKF), a novel adaptive robust cubature Kalman filter (ARCKF) is suggested in this paper. There are two adaptable components in the algorithm. First, an adaptive fading factor addresses the model uncertainty issue brought on by the target's maneuvering turn. Second, an improved Sage-Husa estimation based on the Mahalanobis distance (MD) is suggested to estimate the measurement noise covariance matrix adaptively. The new approach significantly increases the robustness and estimation precision of the HCKF. According to the simulation results, the suggested algorithm is more effective than the conventional HCKF at handling system model errors and abnormal observations.

Research on guidance information extraction technology of strap-down seeker based on adaptive cubature Kalman filter

In comparison to the traditional platform seeker, strap-down seeker, which is firmly connected with rocket-propelled vehicle, has a lot of strength in a smaller size, a simpler structure, and a lower cost. However, because it is fixed on the rocket-propelled vehicle, inertial line-of-sight(LOS) angular velocity demanded by guidance system could not be directly obtained during the rocket-propelled vehicle flight. In this paper, according to the coordinate transformation relationship, the inertial LOS angular velocity decoupling model is established. Aiming at measurement error characteristics of a seeker, a guidance information extraction algorithm which is on basis of the adaptive cubature Kalman filter is studied, and the digital simulation experiment is carried out in combination with the engineering practice. The simulation data demonstrate that the suggested algorithm has a significant effect on improving the filtering estimation stability and accuracy of guidance information extraction and guaranteeing the accuracy of rocket-propelled vehicle hits.

Backward smoothing adaptive SVDCKF integrated navigation algorithm

Aiming at the problem that the Cubature Kalman filter (CKF) algorithm has a non-positive definite of the covariance matrix in GNSS/SINS integrated navigation, which leads to the failure of Cholesky decomposition and the inability to estimate GNSS measurement noise, a backward smoothing adaptive CKF integrated navigation algorithm based on Singular Value Decomposition (SVD) is proposed. Backward smoothing adaptive CKF based on Singular Value Decomposition (BS-A-SVDCKF) algorithm replaces the Cholesky decomposition in standard CKF with SVD, constructs the test threshold for backward smoothing by using the relatively stable noise of the SINS system, and adaptively estimates based on different measurement characteristics when the GNSS measurement noise changes abruptly. Experimental results show that compared with adaptive SVDCKF (A-SVDCKF), backward smoothing SVDCKF (BS-SVDCKF) and SVDCKF, the average positioning accuracy of the proposed algorithm is improved by 32.12%, 57.07% and 68.66%, the average operating efficiency is reduced by 2.19% compared with SVDCKF, and 0.57% and 3.60% higher than that of BS-SVDCKF and A-SVDCKF, which improves the positioning accuracy of the integrated navigation system and ensures real-time.

Research on Pedestrian Indoor Positioning Based on Two-Step Robust Adaptive Cubature Kalman Filter with Smartphone MEMS Sensors.

With the development of location-based service (LBS), indoor positioning based on pedestrian dead reckoning (PDR) has become a hot research topic. Smartphones are becoming more popular for indoor positioning. This paper proposes a two-step robust-adaptive-cubature Kalman filter (RACKF) algorithm based on smartphone micro-electro-mechanical-system (MEMS) sensor fusion for indoor positioning. To estimate pedestrian heading, a quaternion-based robust-adaptive-cubature Kalman filter algorithm is proposed. Firstly, the model noise parameters are adaptively corrected based on the fading-memory-weighting method and the limited-memory-weighting method. The memory window of the limited-memory-weighting algorithm is modified based on the characteristics of pedestrian walking. Secondly, an adaptive factor is constructed based on the partial state inconsistency to overcome filtering-model deviation and abnormal disturbances. Finally, to identify and control the measurement outliers, the robust factor based on maximum-likelihood estimation is introduced into the filtering to enhance the robustness of heading estimation and support more robust dynamic-position estimation. In addition, based on the accelerometer information, a nonlinear model is constructed and the empirical model is used to estimate the step length. Combining heading and step length, the two-step robust-adaptive-cubature Kalman filter is proposed to improve the pedestrian-dead-reckoning method, which enhances the adaptability and robustness of the algorithm and further improves the accuracy of the plane-position solution. The adaptive factor based on the prediction residual and the robust factor based on the maximum-likelihood estimation are introduced into the filter to improve the adaptability and robustness of the filter, reduce the positioning error, and improve the accuracy of the pedestrian-dead-reckoning method. Three different smartphones are used to validate the proposed algorithm in an indoor environment. Additionally, the experimental results confirm the algorithm's effectiveness. From the results of the three smartphones, the root mean square error (RMSE) of the indoor-positioning results obtained by the proposed method is about 1.3-1.7 m.

Adaptive robust maximum correntropy cubature Kalman filter for spacecraft attitude estimation

In this paper, an adaptive robust maximum correntropy cubature Kalman filter (AMCCKF) for spacecraft attitude estimation is proposed. The main aim is to enable the algorithm to resist the influence of noise non-Gaussian and insufficient statistical information on process noise in the system. Based on the maximum correntropy criterion in this study, the robust filtering algorithm is derived by establishing the regression model. In addition, the Cauchy kernel function is used to replace the Gaussian kernel function in the cost function to prevent a singular matrix from appearing in algorithm operation. Furthermore, the process noise is corrected by an adaptive fading factor. The quaternion normalization problem is solved by constructing a minimum constraint cost function using additive quaternion property. The simulation results show that the proposed AMCCKF algorithm has better estimation accuracy and robustness under the conditions of non-Gaussian noise and insufficient statistical information of process noise.