Robust constrained kalman filter algorithm considering time registration for GNSS/Acoustic joint positioning

Kuang, Yingcai; Zhang, Lu; Li, Linyang; Chen, Zhengsheng; Cui, Yang; Wang, Fangchao

doi:10.1016/j.apor.2020.102435

Cited by 3 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If this assumption does not hold and there are many outliers, the Kalman filter may lead to poor performance [3]. Since the standard Kalman filter is not robust against modelling uncertainty and measurement outliers, various kinds of robust Kalman filter design techniques have been developed to tackle this problem [4][5][6][7][8]. Among them, the H∞ filter is perhaps a significant tool since it minimises the H∞ norm of the transfer function between the noise and the estimation error [9].…”

Section: Introductionmentioning

confidence: 99%

Singular value decomposition‐based iterative robust cubature Kalman filtering and its application for integrated global positioning system/strapdown inertial navigation system navigation

Zhang

et al. 2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

The issue of non-linear robust state estimation in the integration of a strapdown inertial navigation system and global positioning system is addressed in this study. Based on the cubature Kalman filtering frame, a non-linear robust filter called a robust cubature Kalman filter (RCKF) was introduced to address the outliers and the inaccurate model. It has been found that the determination of an optimal restriction parameter is crucial for maintaining the robustness and accuracy of the non-linear robust filter. Unfortunately, the value of this restriction parameter is always determined by experience. In this study, an iterative strategy was proposed to adaptively attain the optimal restriction parameter without much previous experience. To improve the computational stability of the iterative non-linear robust filter, a singular value decomposition strategy was adopted simultaneously. Two case studies indicate that the iterative RCKF can achieve greater robustness and accuracy using the methodology discussed in this study. K E Y W O R D S cubature Kalman filter, integrated navigation system, non-linear filter, singular value decompositionThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

show abstract

Section: Introductionmentioning

confidence: 99%

Singular value decomposition‐based iterative robust cubature Kalman filtering and its application for integrated global positioning system/strapdown inertial navigation system navigation

Zhang

et al. 2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

Angle-Weighted trilateration method fusing multi-data processing technologies in indoor scene

Zang,

Jiao,

Wang

et al. 2024

Digital Signal Processing

View full text Add to dashboard Cite

A Fast Operation Method for Predicting Stress in Nonlinear Boom Structures Based on RS–XGBoost–RF Model

Dong,

Su,

et al. 2024

Electronics

View full text Add to dashboard Cite

The expeditious and precise prediction of stress variations in nonlinear boom structures is paramount for ensuring the safe, dependable, and effective operation of pump trucks. Nonetheless, balancing prediction accuracy and efficiency by constructing a suitable machine-learning model remains a challenge in engineering practice. To this end, this paper introduces an interpretable fusion model named RS–XGBoost–RF (Random Search–Extreme Gradient Boosting Tree–Random Forest) and develops an intelligent algorithm for the stress prediction of the nonlinear boom structure of concrete pump trucks. Firstly, an information acquisition system is deployed to collect relevant data from the boom systems of ZLJ5440THBBF 56X-6RZ concrete pump trucks during its operational phase. Data pre-processing is conducted on the 2.4 million sets of acquired data. Then, a sample dataset of typical working conditions is obtained. Secondly, the RS algorithm, RF model, and XGBoost model are selected based on their complementary strengths to construct the fusion model. The model fusion condition is established with a focus on prediction efficiency. By leveraging the synergy between search and prediction mechanisms, the RS–XGBoost model is constructed for the prediction of the master hyperparameters of the RF model. This model uses the random search (RS) process to obtain the mapping between the loss function and the hyperparameters. This mapping relationship is then learned using the XGBoost model, and the hyperparameter value with the smallest loss value is predicted. Finally, the RS–XGBoost–RF model with optimized hyperparameters is employed to achieve rapid stress prediction at various detection points of the nonlinear boom structure. The findings demonstrate that, within the acceptable prediction efficiency for engineering practice, the fitting accuracy (R2) of the RS–XGBoost–RF model consistently exceeds 0.955 across all measurement points, with only a few exceptions. Concerning the stress magnitudes themselves, the mean absolute error (MAE) and root mean square error (RMSE) are maintained within the ranges of 2.22% to 3.91% and 4.79% to 7.85%, respectively. In comparison with RS–RF–RF, RS–RF–XGBoost, and RS–XGBoost–XGBoost, the proposed model exhibits the optimal prediction performance. The method delineated in this paper offers valuable insights for expeditious structural stress prediction in the realm of inherent safety within construction machinery.

show abstract

Robust constrained kalman filter algorithm considering time registration for GNSS/Acoustic joint positioning

Cited by 3 publications

References 28 publications

Singular value decomposition‐based iterative robust cubature Kalman filtering and its application for integrated global positioning system/strapdown inertial navigation system navigation

Singular value decomposition‐based iterative robust cubature Kalman filtering and its application for integrated global positioning system/strapdown inertial navigation system navigation

Angle-Weighted trilateration method fusing multi-data processing technologies in indoor scene

A Fast Operation Method for Predicting Stress in Nonlinear Boom Structures Based on RS–XGBoost–RF Model

Contact Info

Product

Resources

About