Multi-Kernel Maximum Correntropy Kalman Filter for Orientation Estimation

Li, Shilei; Li, Lijing; Shi, Dawei; Zou, Wulin; Duan, Pu; Shi, Ling

doi:10.1109/lra.2022.3176798

Cited by 6 publications

(4 citation statements)

References 45 publications

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“…In this step, data pre-processing using GMKMCKF [28] is discussed. The GMKMCKF is used to exploring the data.…”

Section: B Pre-processing Using Generalized Multi-kernel Maximum Corr...mentioning

confidence: 99%

Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation

Yang Liu

2024

jes

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Tennis is highly explosive, continuous, intense sport that involves numerous continuous short-term explosive actions. This type of training is short-term, high-intensity, and focuses on competitive skills. Athletes must be physically fit and have long-term endurance to excel in technical and tactical skills during competition. In this manuscript, Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation (MMARSCNN-PAM-TT-DM-IR) is proposed. The input data are collected from training levels of technical – sportive actions of 12-14 years old tennis players’ dataset. Initially the input data is preprocessed using Generalized Multi-kernel Maximum Correntropy Kalman Filter for exploring the data. The pre-processed data is given into Gradient Descent Namib Beetle Optimization (GDNBO) for choosing features from the dataset. Then, selected features are given to Dual Tree Complex Discrete Wavelet Transform to extract gray-scale statistical features such as Homogeneity, Entropy, Energy and Smoothness. Finally, the extracted feature attributes are given to MMARSCNN is used to Tennis Teaching Based on Data Mining. In general, MMARSCNN does not express some adaption of optimization strategies for determining optimal parameters to assure accurate Teaching of Tennis. Therefore, Zebra Optimization Algorithm (ZOA) is proposed to enhance weight parameter of MMARSCNN, which precisely Teaching of tennis depend on Data Mining. The proposed model is implemented, its efficacy is assessed utilizing some performance metrics such as accuracy, precision, F1-score, MSE, AUC. The proposed MMARSCNN-PAM-TT-DM-IR method provides 22.55%, 24.72% and 29.63% higher accuracy; 32.66%, 34.97% and 29.57% higher precision; 25.18%, 21.52% and 28.68% higher F1-Score is compared with existing method such as analysis characteristics of Tennis singles matches depend on 5G with data mining technology (AOC-TSM-DM), Tennis online teaching information platform depend on android mobile intelligent terminal (AMIT-TTP-DM), optimization analysis of Tennis players’ physical fitness index depend on data mining with mobile computing (OA-TP-DM-MC) respectively.

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“…In this step, data pre-processing using GMKMCKF [28] is discussed. The GMKMCKF is used to exploring the data.…”

Section: B Pre-processing Using Generalized Multi-kernel Maximum Corr...mentioning

confidence: 99%

Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation

Yang Liu

2024

jes

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“…In this section, the most commonly used filters under non-Gaussian distributions [9], [14], [18] are reviewed.…”

Section: Related Workmentioning

confidence: 99%

“…In [9], unlike the KF, MCKF is able to perform successfully under the non-Gaussian distribution with a defined error threshold and a small kernel bandwidth. Another research [18] has proposed Multi-Kernel maximum correntropy Kalman filter (MKM-CKF). Multivariate non-Gaussian noises and disturbances have been coped with using MKMCKF.…”

Section: Related Workmentioning

confidence: 99%

An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

Candan,

Beke,

Mihaylova

2023

2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper presents a novel approach called the Interacting Multiple Model (IMM)-based Maximum Correntropy Student's T Filter (MCStF), which addresses the challenges posed by non-Gaussian measurement noises. The MCStF demonstrates superior performance compared to the IMM algorithm based on Kalman Filters (KFs) in both simulation environments and real-time systems. The Crazyflie 2.0 nano Unmanned Air Vehicle (UAV) model is used in the simulation validation, and results from 3000 independent Monte Carlo runs are shown. After getting the simulation results under monotonously changed non-Gaussian distribution, their performance results have been compared to each other. The same scenario has been applied in the real-time system using Crazyflie 2.0. Next, results from real-time tests are presented in which the position of Crazyflie 2.0 is estimated online.

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“…On one hand, the kernel bandwidth substantially impacts the performance of the MCC. An improperly sized kernel under MCC may fail to enhance robustness against outliers and might even lead to filter divergence [22]. On the other hand, when the system is perturbed by multi-dimensional non-Gaussian noise, the aforementioned MCC algorithms grapple with numerical challenges due to the emergence of singular matrices [23,24].…”

Section: Introductionmentioning

confidence: 99%

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

Ye,

Lu,

Wang

et al. 2023

Electronics

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

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Multi-Kernel Maximum Correntropy Kalman Filter for Orientation Estimation

Cited by 6 publications

References 45 publications

Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation

Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation

An Interacting Multiple Model Approach Based on Maximum Correntropy Student's T Filter

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

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