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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 extended Kalman filter (AEKF) using extended Kalman filter (EKF) and unscented Kalman filter (UKF) in order to achieve better intelligent utility grid state estimation accuracy. The performance index and the error are indicators used to evaluate the accuracy of the estimation models in this article. An IEEE 37-node test network is used to implement the state estimation models. The state variables considered in this article are the voltage module at the measurement nodes. The results obtained show that the proposed hybrid algorithm has better performance compared to single state estimation methods such as the extended Kalman filter, the unscented Kalman filter, and the weighted least squares (WLS) method.
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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 (September 2, 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, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.
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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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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 (October 7, 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 exerted as a disturbance during the wide driving condition. So, an adaptive sliding mode controller is applied to make vehicle yaw rate to track its reference with robustness against model uncertainties and disturbances and a non-linear estimator based on unscented Kalman filter is used to estimate wheel slip, yaw rate, road friction coefficient, longitudinal and lateral velocities. The estimation algorithm directly uses non-linear equations of the system and does not need the linearization and differentiation. The designed controller, which is insensitive to system uncertainties, offers the adaptive sliding gains to eliminate the precise determination of the bounds of uncertainties. The sliding gain values are determined using a simple adaptation algorithm that does not require extensive computational load. Numerical simulations of various manoeuvres using a non-linear full vehicle model with seven degrees of freedom demonstrate the high effectiveness of the presented controller for improving the vehicle yaw stability and handling performance.
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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 (March 12, 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, conventional coordinate-uncoupled maneuver models and multi-model filtering methods encounter difficulties in accurately tracking highly maneuverable UAVs. To address this, an innovative approach known as 3DCDM-based GRU-MM is introduced for tracking the maneuvering centroid of a UAV swarm. This method employs a multi-model filtering technique assisted by a gated recurrent unit (GRU) network based on a suitable 3D coordinate-coupled dynamic model. The proposed dynamic model represents the centroid’s tangential load, normal load, and roll angle as random processes, from which a nine-dimensional unscented Kalman filter is derived. A GRU is utilized to update the model weights of the multi-model filtering. Additionally, a smoothing-differencing module is presented to extract the maneuvering features from position observations affected by measurement noise. The resulting GRU-MM method achieved a classification accuracy of 99.73%, surpassing that of the traditional IMM algorithm based on the same model. Furthermore, our proposed 3DCDM-based GRU-MM method outperformed the Singer-KF and 3DCDM-based IMM-EKF in terms of the RMSE for position estimation, which provides a basis for further edge computing.
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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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Gao, She Sheng, Wen Hui Wei, and Li Xue. "Near Space Pseudolite Navigation System Design and High-Performance Filtering Algorithm." Applied Mechanics and Materials 411-414 (September 2013): 931–35. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.931.
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This paper analyzes the defects of satellite navigation systems that exist in positioning and precision-guided weapons and pointes out the advantages and military needs of pseudolite. The autonomous navigation nonlinear mathematical model of Near Space Pseudolite SINS/CNS/SAR autonomous navigation system is established. Based on the merits of fading filter, robust adaptive filtering and particle filter, we propose a fading adaptive Unscented Particle Filtering algorithm. The proposed filtering algorithm is applied to SINS/CNS/SAR autonomous navigation system and conducted simulation calculation with the Unscented Kalman filter and particle filter comparison. The results show that the new algorithm that is proposed meets the needs of pseudolite autonomous navigation, and the navigation accuracy is significantly higher than the Unscented Kalman filter and particle filter algorithm.
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Xu, Daxing, Bao Wang, Lu Zhang, and Zhiqiang Chen. "A New Adaptive High-Degree Unscented Kalman Filter with Unknown Process Noise." Electronics 11, no. 12 (June 13, 2022): 1863. http://dx.doi.org/10.3390/electronics11121863.
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Vehicle state, including location and motion information, plays an essential role on the Internet of Vehicles (IoV). Accurately obtaining the system state information is the premise of realizing precise control. However, the statistics of system process noise are often unknown due to the complex physical process. It is challenging to estimate the system state when the process noise statistics are unknown. This paper proposes a new adaptive high-degree unscented Kalman filter based on the improved Sage–Husa algorithm. First, the traditional Sage–Husa algorithm is improved using a high-degree unscented transform. A noise estimator suitable for the high-degree unscented Kalman filter is obtained to estimate the statistics of the unknown process noise. Then, an adaptive high-degree unscented Kalman filter is designed to improve the accuracy and stability of the state estimation system. Finally, the target tracking simulation results verify the proposed algorithm’s effectiveness.
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Xi, Yan Hui, and Hui Peng. "Training Multi-Layer Perceptrons with the Unscented Kalman Particle Filter." Advanced Materials Research 542-543 (June 2012): 745–48. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.745.
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Many Bayesian learning approaches to multi-layer perceptrons (MLPs) parameters optimization have been proposed such as the extended Kalman filter (EKF). In this paper, a sequential approach is applied to train the MLPs. Based on the particle filter, the approach named unscented Kalman particle filter (UPF) uses the unscented Kalman filter as proposal distribution to generate the importance sampling density. The UPF are devised to deal with the high dimensional parameter space that is inherent to neural network models. Simulation results show that the new algorithm performs better than traditional optimization methods such as the extended Kalman filter.
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Li, Chengyi, and Chenglin Wen. "A Novel Extended Unscented Kalman Filter Is Designed Using the Higher-Order Statistical Property of the Approximate Error of the System Model." Actuators 13, no. 5 (May 1, 2024): 169. http://dx.doi.org/10.3390/act13050169.
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In the actual working environment, most equipment models present nonlinear characteristics. For nonlinear system filtering, filtering methods such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Cubature Kalman Filter (CKF) have been developed successively, all of which show good results. However, in the process of nonlinear system filtering, the performance of EKF decreases with an increase in the truncation error and even diverges. With improvement of the system dimension, the sampling points of UKF are relatively few and unrepresentative. In this paper, a novel high-order extended Unscented Kalman Filter (HUKF) based on an Unscented Kalman Filter is designed using the higher-order statistical properties of the approximate error. In addition, a method for calculating the approximate error of the multi-level approximation of the original function under the condition that the measurement is not rank-satisfied is proposed. The effectiveness of the filter is verified using digital simulation experiments.
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John Basha, Furzana, and Kumar Somasundaram. "Rotor Asymmetry Detection in Wound Rotor Induction Motor Using Kalman Filter Variants and Investigations on Their Robustness: An Experimental Implementation." Machines 11, no. 9 (September 14, 2023): 910. http://dx.doi.org/10.3390/machines11090910.
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This paper analyzes the performance of Kalman filter-based estimators for robust filtering and rotor asymmetry detection in wound rotor induction machines (WRIMs) using real-time data. Filter models were designed based on an extended model of WRIMs. The detection of rotor asymmetry was achieved by estimating the states of rotor resistance and speed using four filters. The sensitivity of the parameters under healthy and asymmetry conditions was thoroughly analyzed and categorized as low, medium, and high sensitivity parameters. Robust model-based estimators were designed to minimize the probability of false alarms. The performance analysis demonstrated that the dual unscented Kalman filter (DUKF) outperformed other Kalman filters such as the extended Kalman filter (EKF), dual extended Kalman filter (DEKF), and unscented Kalman filter (UKF) for state estimation of WRIM.
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Hu, Yinquan, Heping Liu, and Hu Huang. "Construction and Method Study of the State of Charge Model for Lithium-Ion Packs in Electric Vehicles Using Ternary Lithium Packs as an Example." World Electric Vehicle Journal 15, no. 2 (January 30, 2024): 43. http://dx.doi.org/10.3390/wevj15020043.
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Accurate and real-time estimation of pack system-level chips is essential for the performance and reliability of future electric vehicles. Firstly, this study constructed a model of a nickel manganese cobalt cell on the ground of the electrochemical process of the packs. Then, it used methods on the grounds of the unscented Kalman filter and unscented Kalman particle filter for system-level chip estimation and algorithm construction. Both algorithms are on the ground of Kalman filters and can handle nonlinear and uncertain system states. In comparative testing, it can be seen that the unscented Kalman filter algorithm can accurately evaluate the system-level chip of the nickel manganese cobalt cell under intermittent discharge conditions. The system-level chip was 0.53 at 1000 s and was reduced to 0.45 at 1500 s. These results demonstrate that the evaluation of the ternary lithium battery pack’s performance is time-dependent and indicate the accuracy of the algorithm used during this time period. These data should be considered in the broader context of the study for a comprehensive understanding of their meaning. In the later stage, the estimation error of the recursive least-squares unscented Kalman particle filter method for system-level chips began to significantly increase, gradually exceeding 1%, with a corresponding root-mean-square error of 0.002171. This indicates that the recursive least-squares optimization algorithm, the unscented Kalman particle filter algorithm, diminished its root mean square error by 27.59%. The unscented Kalman filter and unscented Kalman particle filter are effective in estimating the system-level chip of nickel manganese cobalt cells. However, UPF performs more robustly in handling complex situations, such as pack aging and temperature changes. This study provides a new perspective and method that has a high reference value for pack management systems. This helps to achieve more effective energy management and improve pack life, thereby enhancing the reliability and practicality of electric vehicles.
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Yan, Xiaolong, Guoguang Chen, and Xiaoli Tian. "Two-Step Adaptive Augmented Unscented Kalman Filter for Roll Angles of Spinning Missiles Based on Magnetometer Measurements." Measurement and Control 51, no. 3-4 (April 2018): 73–82. http://dx.doi.org/10.1177/0020294018769828.
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It is critical to measure the roll angle of a spinning missile quickly and accurately. Magnetometers are commonly used to implement these measurements. At present, the estimation of roll angle parameters is usually performed with the unscented Kalman filter algorithm. In this paper, the two-step adaptive augmented unscented Kalman filter algorithm is proposed to calibrate the biaxial magnetometer and circuit measurements quickly, which allows accurate estimates of the missile roll angle. Unlike the existing algorithms, the state vector of the algorithm is based on the missile roll angle parameters and the error factors caused by the magnetometer and the measurement circuit errors. Next, a two-step fast fitting algorithm is used to fit the initial value. After satisfying the stop rule, the state vector of the filter is configured to estimate the roll angle parameters and the calibration parameters. This method is evaluated by running numerous simulations. In the experiment, the algorithm completes the calibration of the magnetometer and the measurement circuit 1 s after the missile launches, with a sampling rate of 1 ms and an output roll attitude angle with a 0.0015 rad precision. The conventional unscented Kalman filter algorithm requires more time to achieve such a high accuracy. The simulation results demonstrate that the proposed two-step augmented unscented Kalman filter outperforms the conventional unscented Kalman filter in its estimation accuracy and convergence characteristics.
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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 (March 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 acceleration constraint, and uses the offline data to obtain steady gain, and then estimates the target state online. The simulation result show that the algorithm can track the target and need less time compared with the traditional unscented Kalman filter.
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Wang, Xinmei, Hui Zhang, Xiaodong Gao, and Rujin Zhao. "The Tobit-Unscented-Kalman-Filter-Based Attitude Estimation Algorithm Using the Star Sensor and Inertial Gyro Combination." Micromachines 14, no. 6 (June 13, 2023): 1243. http://dx.doi.org/10.3390/mi14061243.
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For the orbit operation of spacecraft, due to environmental factors, a star sensor installed on the spacecraft must have data censoring, which greatly reduces the attitude determination ability of the traditional combined-attitude-determination algorithm. To address this problem, this paper proposes an algorithm for high-precision attitude estimation based on a Tobit unscented Kalman filter. This is on the basis of establishing the nonlinear state equation of the integrated star sensor and gyroscope navigation system. The measurement update process of the unscented Kalman filter is improved. The Tobit model is used to describe the gyroscope drift when the star sensor fails. The latent measurement values are calculated using the probability statistics, and the measurement error covariance expression is derived. The proposed design is verified via computer simulations. When the star sensor fails for 15 min, the accuracy of the Tobit unscented Kalman filter based on the Tobit model is improved by approximately 90% compared to the unscented Kalman filter. Based on the results, the proposed filter can effectively estimate the error caused by the gyro drift, and the method is effective and feasible, provided there is theoretical support for the engineering practice.
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Zhang, Yansuo, Shubi Zhang, Yandong Gao, Shijin Li, Yikun Jia, and Minggeng Li. "Adaptive Square-Root Unscented Kalman Filter Phase Unwrapping with Modified Phase Gradient Estimation." Remote Sensing 14, no. 5 (March 2, 2022): 1229. http://dx.doi.org/10.3390/rs14051229.
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Phase unwrapping (PU) is a key program in data processing in the interferometric synthetic aperture radar (InSAR) technique, and its accuracy directly affects the quality of final SAR data products. However, PU in regions with large gradient changes and high noise has always been a difficult problem. To overcome the limitation, this article proposes an adaptive square-root unscented Kalman filter PU method. Specifically, a modified phase gradient estimation (PGE) algorithm is proposed, in which a Butterworth low-pass filter is embedded, and the PGE window can be adaptively adjusted according to phase root-mean-square errors of pixels. Furthermore, the outliers of the PGE results are detected and revised to obtain high-precision vertical and horizontal phase gradients. Finally, the unwrapped phase is calculated by the adaptive square-root unscented Kalman filter method. To the best of our knowledge, this article is the first to combine the modified PGE with an adaptive square-root unscented Kalman filter for PU. Two sets of simulated data and a set of TerraSAR-X/TanDEM-X real data were used for experimental verification. The experimental results demonstrated that the various improvement measures proposed in this article were effective. Additionally, compared with the minimum-cost flow algorithm (MCF), statistical-cost network-flow algorithm (SNAPHU) and unscented Kalman filter PU (UKFPU), the proposed method had better accuracy and model robustness.
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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, the state estimation would converge to the true value rapidly and the accuracy of system state estimation would be improved eventually. The results of simulations and practical tests show that strong tracking unscented FastSLAM (STUFastSLAM) has a significant improvement in accuracy and robustness.
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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, industrial chemical processing and spray coating. The aim is to model the coupled tank system using mixed UKF and DE method to estimate the parameters of the tank. First, a non-linear mathematical model is developed. Next, its parameters are identified using mixed Unscented Kalman Filter (UKF) and Differential Evolution (DE) based on the experimental data. DE algorithm is integrated into the UKF algorithm to optimize the Kalman gain obtained. The obtained results demonstrate good performances.
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Pei, Xiaofei, Zhenfu Chen, Bo Yang, and Duanfeng Chu. "Estimation of states and parameters of multi-axle distributed electric vehicle based on dual unscented Kalman filter." Science Progress 103, no. 1 (October 3, 2019): 003685041988008. http://dx.doi.org/10.1177/0036850419880083.
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Distributed electric drive technology has become an important trend because of its ability to enhance the dynamic performance of multi-axle heavy vehicle. This article presents a joint estimation of vehicle’s state and parameters based on the dual unscented Kalman filter. First, a 12-degrees-of-freedom dynamic model of an 8 × 8 distributed electric vehicle is established. Considering the dynamic variation of some key parameters for heavy vehicle, a real-time parameter estimator is introduced, based on which simultaneous estimation of vehicle’s state and parameters is implemented under the dual unscented Kalman filter framework. Simulation results show that the dual unscented Kalman filter estimator has a high estimation accuracy for multi-axle distributed electric vehicle’s state and key parameters. Therefore, it is reliable for vehicle dynamics control without the influence of unknown or varying parameters.
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Zhao, Kun, Ke Gang Pan, Ai Jun Liu, and Dao Xing Guo. "The Unscented Kalman Filter Estimators for High Dynamic Doppler Shift Environments." Advanced Materials Research 532-533 (June 2012): 1487–91. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1487.
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The Extend Kalman Filter (EKF) is widely used in the tracking of high dynamic Doppler shift trajectories, but it has some flows when it is used to estimate the state of nonlinear systems. In this paper, we apply the Unscented Transformation (UT) based Unscented Kalman Filter (UKF) to the state estimation in the high dynamic Doppler environments. Two versions of the UKF estimators, augmented UKF estimator and nonaugemented UKF estimator are designed. To compare the performance of them, they are applied to tracking a common high dynamic trajectory, and simulation results declare that given different conditions, the performance of the estimators will be different.
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Ullah, Inam, Xin Su, Jinxiu Zhu, Xuewu Zhang, Dongmin Choi, and Zhenguo Hou. "Evaluation of Localization by Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter-Based Techniques." Wireless Communications and Mobile Computing 2020 (October 2, 2020): 1–15. http://dx.doi.org/10.1155/2020/8898672.
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Mobile robot localization has attracted substantial consideration from the scientists during the last two decades. Mobile robot localization is the basics of successful navigation in a mobile network. Localization plays a key role to attain a high accuracy in mobile robot localization and robustness in vehicular localization. For this purpose, a mobile robot localization technique is evaluated to accomplish a high accuracy. This paper provides the performance evaluation of three localization techniques named Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF). In this work, three localization techniques are proposed. The performance of these three localization techniques is evaluated and analyzed while considering various aspects of localization. These aspects include localization coverage, time consumption, and velocity. The abovementioned localization techniques present a good accuracy and sound performance compared to other techniques.
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Zhao, Mujie, Tao Zhang, and Di Wang. "A high precision indoor positioning method based on UKF." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 3639–52. http://dx.doi.org/10.3233/jifs-211810.
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Aiming at the nonlinear filter problem in Ultra Wide Band (UWB) navigation and position, a high-order Unscented Kalman Filter (UKF) position method is proposed. On the one hand, the position and velocity are used as state variables to establish a nonlinear filtering model based on UWB position system. On the other hand, based on the fifth order cubature transform (CT), the analytical solution of the high-order unscented Kalman filter is obtained by introducing a free parameter δ. To verify the effectiveness of the proposed method, the Time of Arrival (TOA) location method, the least square method and fifth order CKF method are introduced as comparison methods. The simulation and experimental results show that the proposed high-order UKF method has good positioning accuracy in both static and dynamic UWB positioning methods.
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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 (August 2017): 499–512. http://dx.doi.org/10.1016/j.asr.2017.03.014.
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Wang, Hao, Yanping Zheng, and Yang Yu. "Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter." Mathematics 9, no. 15 (July 22, 2021): 1733. http://dx.doi.org/10.3390/math9151733.
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In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.
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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 (August 8, 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 state estimation, and a priori measurement estimation. The simulation results of an inverted pendulum and a re-entry vehicle tracking problem clearly demonstrate robust and better performance of this new AFUK[Formula: see text] filter in comparison with those of both UKF and U[Formula: see text]F, appropriately. It is shown that, therefore, the new presented AFUK[Formula: see text] filter can simply eliminate the need for either UKF or U[Formula: see text] F effectively in the presence of Gaussian and/or non-Gaussian noises.
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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 (February 5, 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 noise are still required. In this paper, we propose the recurrent neural-network based Kalman filter, where its Kalman gain is obtained via the proposed GRU-LSTM based neural-network framework that does not need the precise model information as well as the noise covariance information. By the proposed neural-network based Kalman filter, the state estimation performance is enhanced in terms of the tracking error, which is verified through various linear and nonlinear tracking problems with incomplete model and statistical covariance information.
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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 (April 17, 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 algorithms, a new dual Kalman algorithm is proposed in this paper by adding a transfer function and through weighted mutation. The model and algorithm accuracy is verified through working condition experiments. The result shows that: the errors of the three algorithms are all maintained within 0.8% during the initial period and middle stages of the discharge; the maximum error of the improved extension of Kalman algorithm is over 1.5%, that of improved unscented Kalman increases to 5%, and the error of the new dual Kalman algorithm is still within 0.4% during the latter period of the discharge. This indicates that the accuracy and robustness of the new dual Kalman algorithm is better than those of traditional algorithm.
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Zhang, Yong-Gang, Yu-Long Huang, Zhe-Min Wu, and Ning Li. "Moving State Marine SINS Initial Alignment Based on High Degree CKF." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/546107.
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A new moving state marine initial alignment method of strap-down inertial navigation system (SINS) is proposed based on high-degree cubature Kalman filter (CKF), which can capture higher order Taylor expansion terms of nonlinear alignment model than the existing third-degree CKF, unscented Kalman filter and central difference Kalman filter, and improve the accuracy of initial alignment under large heading misalignment angle condition. Simulation results show the efficiency and advantage of the proposed initial alignment method as compared with existing initial alignment methods for the moving state SINS initial alignment with large heading misalignment angle.
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Wang, Junting, Tianhe Xu, and Zhenjie Wang. "Adaptive Robust Unscented Kalman Filter for AUV Acoustic Navigation." Sensors 20, no. 1 (December 20, 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 that the system noise matrices are kept as positive or semi positive. In order to control the influence of gross errors in the acoustic observations, the equivalent gain matrix is constructed to improve the robustness of the adaptive UKF for AUV acoustic navigation based on Huber’s equivalent weight function. The effectiveness of the algorithm is verified by the simulated long baseline positioning experiment of the AUV, as well as the real marine experimental data of the ultrashort baseline positioning of an underwater towed body. The results demonstrate that the adaptive UKF can estimate the system noise through the time-varying noise estimator and avoid the problem of negative definite of the system noise variance matrix. The proposed adaptive robust UKF based on the Sage-Husa filter can further reduce the influence of gross errors while adjusting the system noise, and significantly improve the accuracy and stability of AUV acoustic navigation.
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Xing, Jie, and Peng Wu. "State of Charge Estimation of Lithium-Ion Battery Based on Improved Adaptive Unscented Kalman Filter." Sustainability 13, no. 9 (April 30, 2021): 5046. http://dx.doi.org/10.3390/su13095046.
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State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the system, the traditional unscented Kalman filter (UKF) may fail to estimate SOC due to the non-positive error covariance of the state vector, and the SOC estimation accuracy is not high. Therefore, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed to solve this problem. The IAUKF is composed of the improved unscented Kalman filter (IUKF) that is able to suppress the non-positive definiteness of error covariance and Sage–Husa adaptive filter. The IAUKF can improve the SOC estimation stability and can improve the SOC estimation accuracy by estimating and correcting the system noise statistics adaptively. The IAUKF is verified under the federal urban driving schedule test, and the SOC estimation results are compared with IUKF and UKF. The experimental results show that the IAUKF has higher estimation accuracy and stability, which verifies the effectiveness of the proposed method.
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Mu, Jing, and Chang Yuan Wang. "Iterated Cubature Kalman Filter for State Estimation of Maneuver Reentry Vehicle." Advanced Materials Research 466-467 (February 2012): 1329–33. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.1329.
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We present the new filters named iterated cubature Kalman filter (ICKF). The ICKF is implemented easily and involves the iterate process for fully exploiting the latest measurement in the measurement update so as to achieve the high accuracy of state estimation We apply the ICKF to state estimation for maneuver reentry vehicle. Simulation results indicate ICKF outperforms over the unscented Kalman filter and square root cubature Kalman filter in state estimation accuracy.
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Xu, Wan Li, Zhun Liu, and Jun Hui Liu. "GPS/INS Integrated Navigation Based on Unscented Kalman Filter." Applied Mechanics and Materials 380-384 (August 2013): 3429–33. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3429.
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[Purpose] In GPS/INS integrated navigation, which is widely used in high precision of the real-time navigation, the Extended Kalman Filter (EKF) has become one of the most widely used algorithms. Unfortunately, the EKF is based on a sub-optimal implementation of the recursive Bayesian estimation framework applied to Gaussian random variables. This can seriously affect the accuracy or even lead to divergence of the system. In order to improve the accuracy, we apply the Unscented Transformation to GPS/INS integrated navigation. [Method] This paper optimizes GPS/INS integrated navigation by applying the Unscented Kalman Filter (UKF) algorithm which is based on the Unscented Transformation. [Results] The experimental results show that the UKF has an error reduction of over 10% in every estimator relative to the EKF. [Conclusions] Consequently, the UKF is an effective algorithm to improve the accuracy of GPS/INS integrated navigation.
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Wang, Xun, Zhaokui Wang, and Yulin Zhang. "Stereovision-based relative states and inertia parameter estimation of noncooperative spacecraft." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 7 (June 19, 2018): 2489–502. http://dx.doi.org/10.1177/0954410018782021.
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Autonomous proximity operations have recently become appealing as space missions. In particular, the estimation of the relative states and inertia properties of a noncooperative spacecraft is an important but challenging problem, because there might be poor priori information about the target. Using only stereovision measurements, this study developed an adaptive unscented Kalman filter to estimate the relative states and moment-of-inertia ratios of a noncooperative spacecraft. Because the accuracy of the initial relative states has an effect on the estimation convergence performance, attention was also given to their determination. The target’s body-fixed frame was defined in parallel to the chaser’s initial body-fixed frame, and then the initial relative attitude was known. After formulating kinematic constraint equations between the relative states and multiple points on the target surface, particle swarm optimization was utilized to determine the initial relative angular velocity. The initial relative position was also determined under the assumption that the initial relative translational velocity was known. To estimate the relative states and moment-of-inertia ratios using the adaptive unscented Kalman filter, the relative attitude dynamic model was reformulated by designing a novel transition rule with five moment-of-inertia ratios, described in the defined target’s body-fixed frame. The moment-of-inertia ratios were added to the state space, and a new state equation with variant process noise covariance matrix Q was formulated. The measurement updating errors of the relative states were utilized to adaptively modify Q so that the filter could estimate the relative states and moment-of-inertia ratios in two stages. Numerical simulations of the adaptive unscented Kalman filter with unknown moment-of-inertia ratios and the standard unscented Kalman filter with known moment-of-inertia ratios were conducted to illustrate the performance of the adaptive unscented Kalman filter. The obtained results showed the satisfactory convergence of the estimation errors of both the relative states and moment-of-inertia ratios with high accuracy.
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Cetin, Meric, and Selami Beyhan. "Active fault tolerant control for high-precision positioning of a non-contact mode uncertain atomic force microscopy." Transactions of the Institute of Measurement and Control 42, no. 14 (June 8, 2020): 2632–44. http://dx.doi.org/10.1177/0142331220923771.
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A non-contact mode atomic force microscope with chaotic dynamics may exposed to unknown faults, disturbances or uncertain parameters that are not always be compensated using classical control methods. Therefore, a fault tolerant controller must be designed for accurate tracking of the tip-position of the end-effector. In this paper, first, an unscented Kalman filter is designed for joint estimation of the states and parameters for an atomic force microscopy under process noise. The velocity of the end-effector, sample height and unknown fault are simultaneously estimated by measuring the tip position of randomly excited microscopy. Second, unscented Kalman filtering based model predictive controller is proposed for the accurate tracking of the tip-position. To prevent the disadvantage of the model-based controller design, an uncertainty or unknown fault function of the system is estimated by unscented Kalman filter such that the unmodeled dynamics of the system are compensated while the control signal is produced. Note that the controller voltage being applied to the microscopy is produced based on the estimated states and parameters of the atomic force microscopy. The numerical applications present that satisfactory tracking performance for tip position is obtained by the proposed fault tolerant controller such that extended Kalman filtering-based tracking results are also compared and discussed.
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Zhou, Wu, Chun Xia Zhao, and Mian Hao Zhang. "Fast Kalman SLAM." Applied Mechanics and Materials 44-47 (December 2010): 3174–79. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3174.
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When Simultaneous Localization and Map Building is carried out in complex environments, reduction of computational complexity is a key problem. With a view to the high computational complexity of particle filter, a SLAM solution named ‘Fast Kalman SLAM’ is introduced. Adopting the ‘decomposition’ idea in the FastSLAM algorithm, Fast Kalman SLAM factors the joint SLAM state into a path component and a conditional map component. The robot pose is estimated recursively with Mean Extended Kalman Filter (MEKF) or Unscented Kalman Filter (UKF), while the map with Extended Kalman Filter (EKF). Simulative experiments are carried out to evaluate the performance of the presented algorithm. And Simulation analysis is made for the presented algorithm. The experimental results indicate that the new algorithm reduces computational complexity greatly and ensures estimation accuracy at the same time.
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Lin, Ming, Jaewoo Yoon, and Byeongwoo Kim. "Self-Driving Car Location Estimation Based on a Particle-Aided Unscented Kalman Filter." Sensors 20, no. 9 (April 29, 2020): 2544. http://dx.doi.org/10.3390/s20092544.
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Localization is one of the key components in the operation of self-driving cars. Owing to the noisy global positioning system (GPS) signal and multipath routing in urban environments, a novel, practical approach is needed. In this study, a sensor fusion approach for self-driving cars was developed. To localize the vehicle position, we propose a particle-aided unscented Kalman filter (PAUKF) algorithm. The unscented Kalman filter updates the vehicle state, which includes the vehicle motion model and non-Gaussian noise affection. The particle filter provides additional updated position measurement information based on an onboard sensor and a high definition (HD) map. The simulations showed that our method achieves better precision and comparable stability in localization performance compared to previous approaches.
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Wan, Wenkang, Jingan Feng, Bao Song, and Xinxin Li. "Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation." Energies 14, no. 3 (February 1, 2021): 750. http://dx.doi.org/10.3390/en14030750.
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Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.
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Ren, Hongbin, Sizhong Chen, Gang Liu, and Kaifeng Zheng. "Vehicle State Information Estimation with the Unscented Kalman Filter." Advances in Mechanical Engineering 6 (January 1, 2014): 589397. http://dx.doi.org/10.1155/2014/589397.
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The vehicle state information plays an important role in the vehicle active safety systems; this paper proposed a new concept to estimate the instantaneous vehicle speed, yaw rate, tire forces, and tire kinemics information in real time. The estimator is based on the 3DoF vehicle model combined with the piecewise linear tire model. The estimator is realized using the unscented Kalman filter (UKF), since it is based on the unscented transfer technique and considers high order terms during the measurement and update stage. The numerical simulations are carried out to further investigate the performance of the estimator under high friction and low friction road conditions in the MATLAB/Simulink combined with the Carsim environment. The simulation results are compared with the numerical results from Carsim software, which indicate that UKF can estimate the vehicle state information accurately and in real time; the proposed estimation will provide the necessary and reliable state information to the vehicle controller in the future.
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Zhang, Ping-an, Wei Wang, Min Gao, and Yi Wang. "Square-Root Cubature Kalman Filter Based on H∞ Filter for Attitude Measurement of High-Spinning Aircraft." International Journal of Aerospace Engineering 2021 (May 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/5589691.
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A novel H∞ filter called square-root cubature H∞ Kalman filter is proposed for attitude measurement of high-spinning aircraft. In this method, a combined measurement model of three-axis geomagnetic sensor and gyroscope is used, and the Euler angle algorithm model is used to reduce the state dimension and linearize the state equation, which can reduce the amount of calculation. Simultaneously, the method can be applied to the case of measurement noise uncertainty. By continuously modifying the error limiting parameters to update the measurement noise estimation, the filtering accuracy and robustness can be improved. The square-root forms enjoy a consistently improved numerical stability because all the resulting covariance matrices by QR decomposition are guaranteed to stay positive semidefinite. The algorithm is applied to the simulation experiment of attitude measurement with the combination of geomagnetic sensor and gyroscope and compared with the results of Unscented Kalman filter, cubature Kalman filter, square root cubature Kalman filter, and singular value decomposition cubature Kalman filter, which proves the effectiveness and superiority of the algorithm.
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Jia, Ke, Yifei Pei, Zhaohui Gao, Yongmin Zhong, Shesheng Gao, Wenhui Wei, and Gaoge Hu. "A Quaternion-Based Robust Adaptive Spherical Simplex Unscented Particle Filter for MINS/VNS/GNS Integrated Navigation System." Mathematical Problems in Engineering 2019 (May 20, 2019): 1–13. http://dx.doi.org/10.1155/2019/8532601.
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An improved filtering algorithm-robust adaptive spherical simplex unscented particle filter (RASSUPF) is proposed to achieve high accuracy, induce the amount of computation, and resist the influence of abnormal interference for the MINS/VNS/GNS integrated navigation system. This algorithm adopts spherical simplex unscented transformation (SSUT) to approximate the probability distribution, employs the spherical simplex unscented Kalman filter (SSUKF) to generate the importance sampling density of particle filter, and applies robust and adaptive estimation to control the influence of the abnormal information on the state model and the observation model. Simulation results demonstrate the proposed algorithm can effectively reduce the navigation error, improve the navigation positioning precision, and decrease the computation cost.
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Goll, Stanislaw, and Elena Zakharova. "An active beacon-based leader vehicle tracking system." ACTA IMEKO 8, no. 4 (December 16, 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 position of the beacon in the robot’s local coordinate system, several methods are used, including the least squares method with subsequent exponential smoothing, the linear Kalman filter, the Rauch-Tung-Striebel smoother, the extended Kalman Filter, the unscented Kalman filter, and the particle filter. The experiments were undertaken in order to estimate the estimation method preferable for following the leader’s path.
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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 (September 3, 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 derivative of the Rényi entropy is equal to zero, which means that the Rényi entropy will keep stable. As the temporal derivative of the Rényi entropy is independent of parameter α and is the same as the temporal derivative of the Shannon entropy, the result is the same as for Shannon entropy. Finally, an example of an experiment of falling body tracking by radar using an unscented Kalman filter (UKF) in noisy conditions and a loosely coupled navigation experiment are performed to demonstrate the effectiveness of the conclusion.
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Wang, Xian, Zhengxiang Song, Kun Yang, Xuyang Yin, Yingsan Geng, and Jianhua Wang. "State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries." Energies 12, no. 1 (January 7, 2019): 183. http://dx.doi.org/10.3390/en12010183.
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Lithium-bismuth liquid metal batteries have much potential for stationary energy storage applications, with characteristics such as a large capacity, high energy density, low cost, long life-span and an ability for high current charge and discharge. However, there are no publications on battery management systems or state-of-charge (SoC) estimation methods, designed specifically for these devices. In this paper, we introduce the properties of lithium-bismuth liquid metal batteries. In analyzing the difficulties of traditional SoC estimation techniques for these devices, we establish an equivalent circuit network model of a battery and evaluate three SoC estimation algorithms (the extended Kalman filter, the unscented Kalman filter and the particle filter), using constant current discharge, pulse discharge and hybrid pulse (containing charging and discharging processes) profiles. The results of experiments performed using the equivalent circuit battery model show that the unscented Kalman filter gives the most robust and accurate performance, with the least convergence time and an acceptable computation time, especially in hybrid pulse current tests. The time spent on one estimation with the three algorithms are 0.26 ms, 0.5 ms and 1.5 ms.
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Li, Xiuyuan, Wenxue Gao, and Jiashu Zhang. "A Novel Hybrid Unscented Particle Filter based on Firefly Algorithm for Tightly-Coupled Stereo Visual-Inertial Vehicle Positioning." Journal of Navigation 73, no. 3 (November 11, 2019): 613–27. http://dx.doi.org/10.1017/s0373463319000845.
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This paper presents a hybrid unscented particle filter (UPF) based on the firefly algorithm for tightly-coupled stereo visual-inertial vehicle positioning systems (VIVPS). Compared with standard UPF, this novel approach can achieve similar estimation accuracy with much less computational complexity. To reduce the computational complexity, the time updating of the hybrid unscented Kalman filter is conducted via the formula of standard linear Kalman filter on the basis of the constructed linear/nonlinear mixed filter model. The particle updating of the particle filter is optimised by modified firefly algorithm to reduce the number of particles needed by means of moving particles towards high likelihood regions via the attraction and movement of fireflies, leading to a significant reduction of computational complexity. Experimental results show the average execution time of the proposed approach is 23·8% that of the standard UPF with similar accuracy, indicating the designed method for tightly-coupled stereo VIVPS can better satisfy the real-time requirement of the system.
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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 (October 21, 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 robust particles from the modified distributions in order to estimate the position of targets. Simultaneously, we present the recursion of the optimized Kalman gain to improve the general unscented transform for the velocity estimates. Using the track label, we further integrate them in the framework of the jump Markov model. The simulation results show that the proposed filter has advances in the multi-target tracking scenes. Moreover, the experiments indicate that the filter can track mobile targets with satisfactory results.
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Prakash, Rahul, and Dharmendra Kumar Dheer. "Vehicle state estimation using a maximum likelihood based robust adaptive extended kalman filter considering unknown white Gaussian process and measurement noise signal." Engineering Research Express 5, no. 2 (June 1, 2023): 025066. http://dx.doi.org/10.1088/2631-8695/acd73e.
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Abstract The stochastic nature of noise signals affects the vehicle’s internal states and the outputs, resulting in the difficulty in estimation. The unknown or time-varying nature of noise signals if not taken into account for estimation, the results will diverge and be highly deteriorated. In this paper, a maximum likelihood principle (MLP) based adaptive robust extended kalman filter for estimating the states of the adopted non-linear vehicle model is proposed. An observability test is done for the purpose of estimation. A covariance matching (CM) based robust adaptive high forgetting factor based fault tolerant technique is also employed on the robust adaptive extended and unscented kalman filters for comparison purpose. The Robustness of the filter is analyzed by varying the parameter of the vehicle through a local sensitivity analysis. The results show that the MLP based approach to the extended kalman filter performs well in three simulations for sinusoidal steering, Double Lance Change, J-Turn, Fishhook, Slalom maneuver in comparison to robust adaptive unscented kalman filter. Friction coefficient of 0.8 (dry road) and 0.4 (wet road) is chosen for the simulation. The sideslip angle RMSE value for MLP based estimation is obtained as 2.62e-05, 4.545e-06 for Sine and DLC maneuver.
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Sung, Kwangjae, Hyo-Jong Song, and In-Hyuk Kwon. "A Local Unscented Transform Kalman Filter for Nonlinear Systems." Monthly Weather Review 148, no. 8 (July 14, 2020): 3243–66. http://dx.doi.org/10.1175/mwr-d-19-0228.1.
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Abstract This paper proposes an efficient data assimilation approach based on the sigma-point Kalman filter (SPKF). With a potential for nonlinear filtering applications, the proposed approach, designated as the local unscented transform Kalman filter (LUTKF), is similar to the SPKF in that the mean and covariance of the nonlinear system are estimated by propagating a set of sigma points—also referred to as ensemble members—generated using the scaled unscented transformation (SUT), while making no assumptions with regard to nonlinear models. However, unlike the SPKF, the LUTKF can reduce the influence of observations on distant state variables by employing a localization scheme to suppress spurious correlations between distant locations in the error covariance matrix. Moreover, while the SPKF uses the augmented state vector constructed by concatenating the model states, model noise, and measurement noise, the system state for the LUTKF is not augmented with the random noise variables, thereby providing an accurate state estimate with relatively few sigma points. In sensitivity experiments executed with a 40-variable Lorenz system, the LUTKF required only three sigma points to prevent filter divergence for linear/nonlinear measurement models. Comparisons of the LUTKF and the local ensemble transform Kalman filters (LETKFs) reveal the advantages of the proposed filter in situations that share common features with geophysical data assimilation applications. In particular, the LUTKF shows considerable benefits over LETKFs when assimilating densely spaced observations that are related nonlinearly to the model state and that have high noise levels—such as the assimilation of remotely sensed data from satellites and radars.
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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 (December 17, 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 algorithm. Simulation results show that the proposed scheme achieved better detection gain than the conventional schemes like an equal gain combining (EGC) based soft fusion rule and a Kalman filter (KL) based soft fusion rule under any conditions. Moreover, the proposed scheme achieved the lowest global probability of error compared to both the conventional EGC and KF schemes.
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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 (June 2014): 380–85. http://dx.doi.org/10.1109/jsee.2014.00043.
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Habib, Tamer Mekky. "Magnetometer-Only Kalman Filter Based Algorithms for High Accuracy Spacecraft Attitude Estimation (A Comparative Analysis)." International Journal of Robotics and Control Systems 3, no. 3 (June 7, 2023): 433–48. http://dx.doi.org/10.31763/ijrcs.v3i3.988.
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Kalman Filter (KF) based algorithms are the most frequently employed attitude estimation algorithms. Typically, a fully observable system necessitates the use of two distinct sensor types. Therefore, relying on a single sensor, such as a magnetometer, for spacecraft attitude estimation is deemed to be a challenge. The present investigation centers on utilizing magnetometers as the exclusive sensor. Several KF based estimation algorithms have been designed and evaluated to give the designer of spacecraft Attitude and Orbit Control System (AOCS) the choice of a suitable algorithm for his mission based on quantitative measures. These algorithms are capable of effectively addressing nonlinearity in both process and measurement models. The algorithms under examination encompass the Extended Kalman Filter (EKF), Sequential Extended Kalman Filter (SEKF), Pseudo Linear Kalman Filter (PSELIKA), Unscented Kalman Filter (USKF), and Derivative Free Extended Kalman Filter (DFEKF). The comparison of the distinct algorithms hinges on key performance metrics, such as estimation error for each axis, computation time, and convergence rate. The resulting algorithms provide numerous benefits, such as diverse levels of high estimation accuracy (with estimation errors ranging from 0.014o to 0.14o), varying computational demands (execution time ranges from 0.0536s to 0.0584s), and the capability to converge despite large initial attitude estimation errors (which reached 170o). These properties render the algorithms appropriate for utilization by spacecraft designers in all operational modes, supplying high-precision attitude estimations better than (0.5o) despite high magnetometer noise levels, which reached (200 nT).