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Journal articles on the topic 'Unscented Kalman filter grand gain'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Matsuura, Tsubasa, Masahiro Matsushita, Gan Chen, and Isao Takami. "Gain-scheduled Control Using Unscented Kalman Filter." Proceedings of Conference of Tokai Branch 2019.68 (2019): 316. http://dx.doi.org/10.1299/jsmetokai.2019.68.316.

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He, Xiaoyou, Yu Su, and Yuhe Qiu. "An Improved Unscented Kalman Filter for Maneuvering Target Tracking*." Journal of Physics: Conference Series 2216, no. 1 (2022): 012010. http://dx.doi.org/10.1088/1742-6596/2216/1/012010.

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Abstract The photoelectric pod provides angular information and distance information for the UAV (Unmanned Aerial Vehicle), and the UAV uses it to estimate the status information of the moving target. Since the measurement information of the photoelectric pod is the angle of sight and relative distance, the measurement equation contains some nonlinear functions in the Cartesian coordinate system, and the output frequency of the photoelectric pod is low. The improved unscented Kalman filter combines the function of prediction and correction, introduces the prior information of the target accele

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Yem Souhe, Felix Ghislain, Alexandre Teplaira Boum, Pierre Ele, Camille Franklin Mbey, and Vinny Junior Foba Kakeu. "A Novel Smart Method for State Estimation in a Smart Grid Using Smart Meter Data." Applied Computational Intelligence and Soft Computing 2022 (May 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/7978263.

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Smart grids have brought new possibilities in power grid operations for control and monitoring. For this purpose, state estimation is considered as one of the effective techniques in the monitoring and analysis of smart grids. State estimation uses a processing algorithm based on data from smart meters. The major challenge for state estimation is to take into account this large volume of measurement data. In this article, a novel smart distribution network state estimation algorithm has been proposed. The proposed method is a combined high-gain state estimation algorithm named adaptive extende

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Liang, Yunpei, Jiahui Dai, Kequan Wang, Xiaobo Li, and Pengcheng Xu. "A Strong Tracking SLAM Algorithm Based on the Suboptimal Fading Factor." Journal of Sensors 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/9684382.

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This paper proposes an innovative simultaneous localization and mapping (SLAM) algorithm which combines a strong tracking filter (STF), an unscented Kalman filter (UKF), and a particle filter (PF) to deal with the low accuracy of unscented FastSLAM (UFastSLAM). UFastSLAM lacks the capacity for online self-adaptive adjustment, and it is easily influenced by uncertain noise. The new algorithm updates each Sigma point in UFastSLAM by an adaptive algorithm and obtains optimized filter gain by the STF adjustment factor. It restrains the influence of uncertain noise and initial selection. Therefore,

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Legowo, Ari, Zahratu H. Mohamad, and Hoon Cheol Park. "Mixed Unscented Kalman Filter and Differential Evolution for Parameter Identification." Applied Mechanics and Materials 256-259 (December 2012): 2347–53. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.2347.

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This paper presents parameters estimation techniques for coupled industrial tanks using the mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) method. UKF have known to be a typical estimation technique used to estimate the state vectors and parameters of nonlinear dynamical systems and DE is one of the most powerful stochastic real-parameter optimization algorithms. Meanwhile, liquid tank systems play important role in industrial application such as in food processing, beverage, dairy, filtration, effluent treatment, pharmaceutical industry, water purification system, industr

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Tehrani, Mohammad, Nader Nariman-zadeh, and Mojtaba Masoumnezhad. "Adaptive fuzzy hybrid unscented/H-infinity filter for state estimation of nonlinear dynamics problems." Transactions of the Institute of Measurement and Control 41, no. 6 (2018): 1676–85. http://dx.doi.org/10.1177/0142331218787607.

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In this paper, a new hybrid unscented Kalman (UKF) and unscented [Formula: see text](U[Formula: see text]F) filter is presented that can adaptively adjust its performance better than that of either UKF and/or U[Formula: see text], accordingly. In this way, two Takagi-Sugeno-Kang (TSK) fuzzy logic systems are presented to adjust automatically some weights that combine those UK and U[Formula: see text] filters, independent of the dynamics of the problem. Such adaptive fuzzy hybrid unscented Kalman/[Formula: see text] filter (AFUK[Formula: see text]) is based on the combination of gain, a priori

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Kim, DongBeom, Daekyo Jeong, Jaehyuk Lim, Sawon Min, and Jun Moon. "Application of Recurrent Neural-Network based Kalman Filter for Uncertain Target Models." Journal of the Korea Institute of Military Science and Technology 26, no. 1 (2023): 10–21. http://dx.doi.org/10.9766/kimst.2023.26.1.010.

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For various target tracking applications, it is well known that the Kalman filter is the optimal estimator(in the minimum mean-square sense) to predict and estimate the state(position and/or velocity) of linear dynamical systems driven by Gaussian stochastic noise. In the case of nonlinear systems, Extended Kalman filter(EKF) and/or Unscented Kalman filter(UKF) are widely used, which can be viewed as approximations of the(linear) Kalman filter in the sense of the conditional expectation. However, to implement EKF and UKF, the exact dynamical model information and the statistical information of

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Fan, Yongcun, Haotian Shi, Shunli Wang, Carlos Fernandez, Wen Cao, and Junhan Huang. "A Novel Adaptive Function—Dual Kalman Filtering Strategy for Online Battery Model Parameters and State of Charge Co-Estimation." Energies 14, no. 8 (2021): 2268. http://dx.doi.org/10.3390/en14082268.

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This paper aims to improve the stability and robustness of the state-of-charge estimation algorithm for lithium-ion batteries. A new internal resistance-polarization circuit model is constructed on the basis of the Thevenin equivalent circuit to characterize the difference in internal resistance between charge and discharge. The extended Kalman filter is improved through adding an adaptive noise tracking algorithm and the Kalman gain in the unscented Kalman filter algorithm is improved by introducing a dynamic equation. In addition, for benignization of outliers of the two above-mentioned algo

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9

Cao, Lu, Weiwei Yang, Hengnian Li, Zhidong Zhang, and Jianjun Shi. "Robust double gain unscented Kalman filter for small satellite attitude estimation." Advances in Space Research 60, no. 3 (2017): 499–512. http://dx.doi.org/10.1016/j.asr.2017.03.014.

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10

Wang, Junting, Tianhe Xu, and Zhenjie Wang. "Adaptive Robust Unscented Kalman Filter for AUV Acoustic Navigation." Sensors 20, no. 1 (2019): 60. http://dx.doi.org/10.3390/s20010060.

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Autonomous underwater vehicle (AUV) acoustic navigation is challenged by unknown system noise and gross errors in the acoustic observations caused by the complex marine environment. Since the classical unscented Kalman filter (UKF) algorithm cannot control the dynamic model biases and resist the influence of gross errors, an adaptive robust UKF based on the Sage-Husa filter and the robust estimation technique is proposed for AUV acoustic navigation. The proposed algorithm compensates the system noise by adopting the Sage-Husa noise estimation technique in an online manner under the condition t

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Goll, Stanislaw, and Elena Zakharova. "An active beacon-based leader vehicle tracking system." ACTA IMEKO 8, no. 4 (2019): 33. http://dx.doi.org/10.21014/acta_imeko.v8i4.685.

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This article focuses on mobile robot convoying along a path travelled by a certain leader carrying the active ultrasonic beacon. The robot is equipped with the three-dimensional receiver array in order to receive both the ultrasonic wave and the RF wave marking the beginning of the measurement cycle. To increase measurement reliability, each receiver contains two independent measurement channels with automatic gain control. The distance measurements are pre-processed in order to identify the artefacts and then either remove them or replace them with the interpolated value. To estimate the posi

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Luo, Yarong, Chi Guo, Shengyong You, and Jingnan Liu. "A Novel Perspective of the Kalman Filter from the Rényi Entropy." Entropy 22, no. 9 (2020): 982. http://dx.doi.org/10.3390/e22090982.

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Rényi entropy as a generalization of the Shannon entropy allows for different averaging of probabilities of a control parameter α. This paper gives a new perspective of the Kalman filter from the Rényi entropy. Firstly, the Rényi entropy is employed to measure the uncertainty of the multivariate Gaussian probability density function. Then, we calculate the temporal derivative of the Rényi entropy of the Kalman filter’s mean square error matrix, which will be minimized to obtain the Kalman filter’s gain. Moreover, the continuous Kalman filter approaches a steady state when the temporal derivati

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13

Li, Bo, Huawei Yi, and Xiaohui Li. "Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking." Measurement and Control 52, no. 9-10 (2019): 1567–78. http://dx.doi.org/10.1177/0020294019877494.

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Multi-target tracking is widely applied in video surveillance systems. As we know, although the standard particle cardinalized probability hypothesis density filter can estimate state of targets, it is difficult to define the proposal distribution function in prediction stage. Since the robust particles cannot be effectively drawn, the actual tracking accuracy should be enhanced. In this paper, an innovative unscented transform–based particle cardinalized probability hypothesis density filter is derived. Considering the different state spaces, we use the auxiliary particle method and then draw

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Choukroun, Daniel, Lotan Cooper, and Nadav Berman. "Stochastic H∞ Filtering of the Attitude Quaternion." Sensors 24, no. 24 (2024): 7971. https://doi.org/10.3390/s24247971.

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Several stochastic H∞ filters for estimating the attitude of a rigid body from line-of-sight measurements and rate gyro readings are developed. The measurements are corrupted by white noise with unknown variances. Our approach consists of estimating the quaternion while attenuating the transmission gain from the unknown variances and initial errors to the current estimation error. The time-varying H∞ gain is computed by solving algebraic and differential linear matrix inequalities for a given transmission threshold, which is iteratively lowered until feasibility fails. Thanks to the bilinear s

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15

Amin, Md, Md Rahman, Mohammad Hossain, Md Islam, Kazi Ahmed, and Bikash Miah. "Unscented Kalman Filter Based on Spectrum Sensing in a Cognitive Radio Network Using an Adaptive Fuzzy System." Big Data and Cognitive Computing 2, no. 4 (2018): 39. http://dx.doi.org/10.3390/bdcc2040039.

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In this paper, we proposed the unscented Kalman filter (UKF) based on cooperative spectrum sensing (CSS) scheme in a cognitive radio network (CRN) using an adaptive fuzzy system—in this proposed scheme, firstly, the UKF to apply the nonlinear system which is used to minimize the mean square estimation error; secondly, an adaptive fuzzy logic rule based on an inference engine to estimate the local decisions to detect a licensed primary user (PU) that is applied at the fusion center (FC). After that, the FC makes a global decision by using a defuzzification procedure based on a proposed algorith

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Lv, Jiechao, Baochen Jiang, Xiaoli Wang, Yirong Liu, and Yucheng Fu. "Estimation of the State of Charge of Lithium Batteries Based on Adaptive Unscented Kalman Filter Algorithm." Electronics 9, no. 9 (2020): 1425. http://dx.doi.org/10.3390/electronics9091425.

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The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore,

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17

Liu, Changyun, Penglang Shui, Gang Wei, and Song Li. "Modified unscented Kalman filter using modified filter gain and variance scale factor for highly maneuvering target tracking." Journal of Systems Engineering and Electronics 25, no. 3 (2014): 380–85. http://dx.doi.org/10.1109/jsee.2014.00043.

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Yang, Yi, Fei Li, Yi Gao, and Yanhui Mao. "Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter." Sensors 20, no. 7 (2020): 1897. http://dx.doi.org/10.3390/s20071897.

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In the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain the accurate attitude parameters of the steering drilling tool, a new method for multi-sensor dynamic attitude combined measurement is presented. Firstly, by using a triaxial accelerometer and triaxial magnetometer measurement system, the nonlinear model based on the quaternion is established. Then,

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19

Wang, Zheng Jun, Jun Zheng Wang, Hao Wang, and Jiang Bo Zhao. "Model Parameter Adaptive Sliding Mode Model-Following Position Control of PMSM." Advanced Materials Research 466-467 (February 2012): 1089–94. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1089.

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This paper aims at improving the robustness of the PMSM position control system with the parameter variation and load disturbance. A novel control strategy utilizing sliding mode model-following control (SMMFC) with the adaptive parameters observed by dual unscented Kalman filter (DUKF) observer is proposed. The switching gain of sliding mode is designed including the observed states of the system to suppress the chattering. The experimental results show that the robustness has been improved by sliding mode control, and the chattering has been well suppressed by switching gain adaptation depen

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20

Liu, Huajun, Liwei Xia, and Cailing Wang. "Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network." Sensors 19, no. 7 (2019): 1596. http://dx.doi.org/10.3390/s19071596.

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Tracking maneuvering targets is a challenging problem for sensors because of the unpredictability of the target’s motion. Unlike classical statistical modeling of target maneuvers, a simultaneous optimization and feedback learning algorithm for maneuvering target tracking based on the Elman neural network (ENN) is proposed in this paper. In the feedback strategy, a scale factor is learnt to adaptively tune the dynamic model’s error covariance matrix, and in the optimization strategy, a corrected component of the state vector is learnt to refine the final state estimation. These two strategies

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21

Battiston, Adrian, Inna Sharf, and Meyer Nahon. "Attitude estimation for collision recovery of a quadcopter unmanned aerial vehicle." International Journal of Robotics Research 38, no. 10-11 (2019): 1286–306. http://dx.doi.org/10.1177/0278364919867397.

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An extensive evaluation of attitude estimation algorithms in simulation and experiments is performed to determine their suitability for a collision recovery pipeline of a quadcopter unmanned aerial vehicle. A multiplicative extended Kalman filter (MEKF), unscented Kalman filter (UKF), complementary filter, [Formula: see text] filter, and novel adaptive varieties of the selected filters are compared. The experimental quadcopter uses a PixHawk flight controller, and the algorithms are implemented using data from only the PixHawk inertial measurement unit (IMU). Performance of the aforementioned

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Chien, Chiang-Heng, Wei-Yen Wang, Jun Jo, and Chen-Chien Hsu. "Enhanced Monte Carlo localization incorporating a mechanism for preventing premature convergence." Robotica 35, no. 7 (2016): 1504–22. http://dx.doi.org/10.1017/s026357471600028x.

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SUMMARYIn this paper, we propose an enhanced Monte Carlo localization (EMCL) algorithm for mobile robots, which deals with the premature convergence problem in global localization as well as the estimation error existing in pose tracking. By incorporating a mechanism for preventing premature convergence (MPPC), which uses a “reference relative vector” to modify the weight of each sample, exploration of a highly symmetrical environment can be improved. As a consequence, the proposed method has the ability to converge particles toward the global optimum, resulting in successful global localizati

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23

Riva, Mauro H., Matthias Dagen, and Tobias Ortmaier. "Adaptive High-Gain observer for joint state and parameter estimation: A comparison to Extended and Unscented Kalman filter." IFAC Proceedings Volumes 47, no. 3 (2014): 8558–63. http://dx.doi.org/10.3182/20140824-6-za-1003.01609.

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Wang, Weichao, Yutaka Sasaki, Yoshifumi Zoka, Naoto Yorino, Ahmed Bedawy, and Seiji Kawauchi. "Adaptive MPC-based load frequency control for microgrids with renewable energy." HKIE Transactions 31, no. 2 (2024): 1–10. https://doi.org/10.33430/v31n2icee23-jy103.

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In this paper, a novel load frequency control (LFC) approach based on adaptive model predictive control (AMPC) is proposed for a microgrid system (MG) with distributed energy resources. A simplified internal prediction model (firstlag system consisting of a gain and a time constant) is applied to online state estimation of the AMPC. The parameters are updated iteratively using unscented Kalman filter (UKF). The MG model with high penetration of renewable energy sources (RESs), such as photovoltaic (PV) and wind turbine (WT) power generations, is carried out to verify the effectiveness of the p

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Bagheri, Ahmad, Shahram Azadi, and Abbas Soltani. "A combined use of adaptive sliding mode control and unscented Kalman filter estimator to improve vehicle yaw stability." Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics 231, no. 2 (2016): 388–401. http://dx.doi.org/10.1177/1464419316673960.

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In this paper, an adaptive sliding mode controller is proposed to improve the vehicle yaw stability and enhance the lateral motion by direct yaw moment control method using active braking systems. As the longitudinal and lateral velocities of the vehicles as well as many other vehicle dynamics variables cannot be measured in a cost-efficient way, a robust control method combined with a state estimator is required to guarantee the system stability. Furthermore, some parameters such as the tyre–road friction coefficient undergo frequent changes, and the aerodynamics resistance forces are often e

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Qi, Liangwen, Liming Zheng, Xingzhi Bai, Qin Chen, Jiyao Chen, and Yan Chen. "Nonlinear Maximum Power Point Tracking Control Method for Wind Turbines Considering Dynamics." Applied Sciences 10, no. 3 (2020): 811. http://dx.doi.org/10.3390/app10030811.

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A combined strategy of torque error feed-forward control and blade-pitch angle servo control is proposed to improve the dynamic power capture for wind turbine maximum power point tracking (MPPT). Aerodynamic torque is estimated using the unscented Kalman filter (UKF). Wind speed and tip speed ratio (TSR) are estimated using the Newton–Raphson method. The error between the estimated aerodynamic torque and the steady optimal torque is used as the feed-forward signal to control the generator torque. The gain parameters in the feed-forward path are nonlinearly regulated by the estimated generator

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Zhang, Hengbo, Xin Liu, Qingfeng Dou, and Qiongyao Han. "Multi-Source Error Calibration and Observability Analysis of Integrated Inertial Navigation System/Polarization Sensor Navigation System." Journal of Physics: Conference Series 2456, no. 1 (2023): 012005. http://dx.doi.org/10.1088/1742-6596/2456/1/012005.

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Abstract Accurate polarization information acquisition is the key technique of the integrated inertial navigation system (INS)/polarization sensor (PS) navigation system. To solve the problem of poor performance of polarization sensors that real-time error calibration by using a rotary table, we propose a fast calibration method that polarization sensor combined with inertial navigation systems. Firstly, we construct the calibration model of polarization sensor. A tightly coupled INS/PS calibration model is proposed, specifically, the polarization sensor errors, which are the intensity gain co

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Chen, L., G. H. Wang, S. Y. Jia, and I. Progri. "Attitude Bias Conversion Model for Mobile Radar Error Registration." Journal of Navigation 65, no. 4 (2012): 651–70. http://dx.doi.org/10.1017/s0373463312000239.

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Besides offset biases (such as range, the gain of range, azimuth, and elevation biases), for mobile radars, platform attitude biases (such as yaw, pitch, and roll biases) induced by the accumulated errors of the Inertial Measurement Units (IMU) of the Inertial Navigation System (INS) can also influence radar measurements. Both kinds of biases are coupled. Based on the analyses of the coupling influences and the observability of 3-D radars’ error registration model, in the article, an Attitude Bias Conversion Model (ABCM) based on Square Root Unscented Kalman Filter (SRUKF) is proposed. ABCM ca

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Chen, Yudi, Xiangyu Liu, Changqing Li, Jiao Zhu, Min Wu, and Xiang Su. "UAV Swarm Centroid Tracking for Edge Computing Applications Using GRU-Assisted Multi-Model Filtering." Electronics 13, no. 6 (2024): 1054. http://dx.doi.org/10.3390/electronics13061054.

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When an unmanned aerial vehicles (UAV) swarm is used for edge computing, and high-speed data transmission is required, accurate tracking of the UAV swarm’s centroid is of great significance for the acquisition and synchronization of signal demodulation. Accurate centroid tracking can also be applied to accurate communication beamforming and angle tracking, bringing about a reception gain. Group target tracking (GTT) offers a suitable framework for tracking the centroids of UAV swarms. GTT typically involves accurate modeling of target maneuvering behavior and effective state filtering. However

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Halil, Ersin Soken, and Hajiyev Chingiz. "Robust UKF Insensitive to Measurement Faults for Pico Satellite Attitude Estimation." International Journal of Mechanical, Industrial and Aerospace Sciences 3.0, no. 8 (2010). https://doi.org/10.5281/zenodo.1332062.

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In the normal operation conditions of a pico satellite, conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study, introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight and

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31

Tang, Yuhang, Wei Liu, Jinkun Zhu, Jing Lei, and Haoying Mo. "Robust Beam Tracking for 3D Manoeuvrable UAV in DFRC Systems." Electronics Letters 61, no. 1 (2025). https://doi.org/10.1049/ell2.70211.

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ABSTRACTDual‐functional radar‐communication (DFRC) will be a key technology in future sixth‐generation (6G) network. Specifically, an integrated radar and communication platform (IRCP) equipped with full‐dimensional antenna arrays can execute 3D beamforming, which can effectively minimize interference for communicating with unmanned aerial vehicles (UAVs). However, a significant challenge in fully exploiting 3D beamforming gain is the IRCP's ability to precisely track manoeuvrable UAVs. In this letter, we propose a novel interacting multiple model with enhanced unscented Kalman filter algorith

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Rahimi, Hossein, Amir Ali Nikkhah, and Kaveh Hooshmandi. "A fast alignment of marine strapdown inertial navigation system based on adaptive unscented Kalman Filter." Transactions of the Institute of Measurement and Control, June 29, 2020, 014233122093429. http://dx.doi.org/10.1177/0142331220934293.

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This study has presented an efficient adaptive unscented Kalman filter (AUKF) with the new measurement model for the strapdown inertial navigation system (SINS) to improve the initial alignment under the marine mooring conditions. Conventional methods of the accurate alignment in the ship’s SINS usually fail to succeed within an acceptable period of time due to the components of external perturbations caused by the movement of sea waves and wind waves. To speed up convergence, AUKF takes into account the impact of the dynamic acceleration on the filter and its gain adaptively tuned by consider

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Bai, Xingzhen, Xinlei Zheng, Leijiao Ge, Feiyu Qin, and Yuanliang Li. "Event-Triggered Forecasting-Aided State Estimation for Active Distribution System With Distributed Generations." Frontiers in Energy Research 9 (July 23, 2021). http://dx.doi.org/10.3389/fenrg.2021.707183.

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In this study, the forecasting-aided state estimation (FASE) problem for the active distribution system (ADS) with distributed generations (DGs) is investigated, considering the constraint of data transmission. First of all, the system model of the ADS with DGs is established, which expands the scope of the ADS state estimation from the power network to the DGs. Moreover, in order to improve the efficiency of data transmission under the limited communication bandwidth, a component-based event-triggered mechanism is employed to schedule the data transmission from the measurement terminals to th

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Zamani, Vahid, Shaghayegh Abtahi, Yuxiang Chen, and Yong Li. "Parameter-input estimation of RC thermal models of buildings using unscented Kalman filter and nonlinear least square method." Indoor and Built Environment, August 15, 2024. http://dx.doi.org/10.1177/1420326x241270775.

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Effective building energy management (e.g. temperature control strategies) necessitates reliable and computationally efficient building thermal models. One type of them is the resistor–capacitor (RC) model. However, estimating model parameters and inputs (e.g. solar heat gain) simultaneously is challenging, especially when some of the temperature states are missing due to instrumentation limitations and/or sensor malfunctions. The present study utilizes unscented Kalman filter (UKF) and nonlinear least squares (NLSs) methods for parameters and input estimation of RC models with possible unavai

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Fall, Mamadou, Chunmei Yu, Paul Takyi-Aninakwa, Shunli Wang, Tofik Seid Ali, and Liya Zhang. "A multi-measurement exponential gain unscented Kalman filter-based state of charge estimation for lithium-ion batteries with temperature adaptability." Ionics, May 8, 2025. https://doi.org/10.1007/s11581-025-06350-w.

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Chen, Ye, Guoliang Tao, and Yitao Yao. "A dual adaptive robust control for nonlinear systems with parameter and state estimation." Measurement and Control, October 10, 2023. http://dx.doi.org/10.1177/00202940231200956.

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Stabilization and learning are imperative to the high-performance feedback control of nonlinear systems. A dual adaptive robust control (DARC) scheme is proposed for nonlinear systems with model uncertainties to achieve a desired level of performance. Only the output of the nonlinear system is accessible in this work, all the states and parameters are learned online. Firstly, the DARC uses the prior physical bounds of systems to design a discontinuous projection with update rate limits which confines the bounds of parameter and state estimation. Then robustness of the nonlinear system can be g

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Liang, Zhenhu, Dihuan Wang, Xing Jin, et al. "Tracking the effects of propofol, sevoflurane and (S)-ketamine anesthesia using an unscented kalman filter-based neural mass model." Journal of Neural Engineering, March 9, 2023. http://dx.doi.org/10.1088/1741-2552/acc2e8.

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Abstract Objective: Neural mass model (NMM) has been widely used to investigate the neurophysiological mechanisms of anesthestic drugs induced general anesthesia (GA). However, whether the parameters of NMM could track the effects of anesthesia still unknown.&#xD;Approach: We proposed using the cortical NMM (CNMM) to infer the potential neurophysiological mechanism of three different anesthetic drugs (i.e., propofol, sevoflurane, and (S)-ketamine) induced GA, and we employed unscented Kalman filter (UKF) to track any change in raw electroencephalography (rEEG) in frontal area during GA. We

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Yang, Yi, Xueyao Wang, Nan Zhang, Zhaohui Gao, and Yingliang Li. "Artificial neural network based on strong track and square root UKF for INS/GNSS intelligence integrated system during GPS outage." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-64918-4.

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AbstractWhen INS/GNSS (inertial navigation system/global navigation satellite system) integrated system is applied, it will be affected by the insufficient number of visible satellites, and even the satellite signal will be lost completely. At this time, the positioning error of INS accumulates with time, and the navigation accuracy decreases rapidly. Therefore, in order to improve the performance of INS/GNSS integration during the satellite signals interruption, a novel learning algorithm for neural network has been presented and used for intelligence integrated system in this article. First

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