An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise

Dong, Xiangxiang; Chisci, Luigi; Cai, Yunze

doi:10.3390/s20236757

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…The machine learning technique has facilitated the identification of UIs and state estimation, and various methods, such as expectation-maximization (EM) and variational Bayesian (VB) techniques, have been utilized for this purpose [25,26]. While the VB approximation is a distribution-estimation method [27], the EM algorithm is focused on point-estimation. Both techniques use iterative optimization to obtain the solutions.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Modeling for Batch Polymerization Reactors with Unknown Inputs

Liu

Luan

2023

Sensors

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While system identification methods have developed rapidly, modeling the process of batch polymerization reactors still poses challenges. Therefore, designing an intelligent modeling approach for these reactors is important. This paper focuses on identifying actual models for batch polymerization reactors, proposing a novel recursive approach based on the expectation-maximization algorithm. The proposed method pays special attention to unknown inputs (UIs), which may represent modeling errors or process faults. To estimate the UIs of the model, the recursive expectation-maximization (EM) technique is used. The proposed algorithm consists of two steps: the E-step and the M-step. In the E-step, a Q-function is recursively computed based on the maximum likelihood framework, using the UI estimates from the previous time step. The Kalman filter is utilized to calculate the estimates of the states using the measurements from sensor data. In the M-step, analytical solutions for the UIs are found through local optimization of the recursive Q-function. To demonstrate the effectiveness of the proposed algorithm, a practical application of modeling batch polymerization reactors is presented. The performance of the proposed recursive EM algorithm is compared to that of the augmented state Kalman filter (ASKF) using root mean squared errors (RMSEs). The RMSEs obtained from the proposed method are at least 6.52% lower than those from the ASKF method, indicating superior performance.

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Section: Introductionmentioning

confidence: 99%

Intelligent Modeling for Batch Polymerization Reactors with Unknown Inputs

Liu

Luan

2023

Sensors

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“…To reduce the interference of unknown and time‐varying noise information, Dan et al (2019) proposed a robust Dempster–Shafer (D‐S) fusion algorithm, which uses the product of the inverse‐gamma models to approximate the unknown covariance matrix of the measurement noise, making full use of the measurement noise and the covariance of local state estimates solves the problem of trajectory fusion for disordered distributed sensors with unknown measurement noise. X. Dong et al (2020) proposed an adaptive variational Bayesian Student's t ‐based cubature information filter algorithm to solve the state estimation and information fusion problems of nonlinear systems with heavy tail measurement noise.…”

Section: Introductionmentioning

confidence: 99%

“…X. Dong et al (2020) proposed an adaptive variational Bayesian Student's t-based cubature information filter algorithm to solve the state estimation and information fusion problems of nonlinear systems with heavy tail measurement noise.…”

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confidence: 99%

Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

Wei

Zhang

2022

Journal of Field Robotics

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Dead reckoning accuracy determines the operation quality of pipeline mobile robot to a certain extent. With the progress of the industry, the research on the dead reckoning system of pipeline robot is more and more in-depth, but how to improve dead reckoning accuracy is still a difficult problem for pipeline mobile robot. In this paper, to overcome the sensor defects of the traditional dead reckoning system, an error backpropagation neural network (BPNN) is introduced to compensate the sensor error caused by static drift and poor dynamic response. To reduce the interference of noise information, the covariance matching technique based on the Adaptive Neuro-Fuzzy Inference System is used to estimate the measurement noise information. And then the two are combined in the algorithm based on the extended Kalman Filter (EKF) framework for data fusion. An EKF algorithm based on adaptive neural fuzzy combined with BPNN is proposed. Finally, a complete three-dimensional dead reckoning system is established by coordinate transformation. The motion experiment of the quadruped wall-climbing robot in the pipeline proves that the proximity L of the calculated trajectory is not more than 6.00%, and the lowest is 1.07%, which effectively improves the reliability, robustness, and positioning accuracy of the dead reckoning.

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Centralized and distributed adaptive cubature information filters for multi-sensor systems with unknown probability of measurement loss

Yang

2023

Information Sciences

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An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise

Cited by 5 publications

References 42 publications

Intelligent Modeling for Batch Polymerization Reactors with Unknown Inputs

Intelligent Modeling for Batch Polymerization Reactors with Unknown Inputs

Three‐dimensional dead reckoning of wall‐climbing robot based on information fusion of compound extended Kalman filter

Centralized and distributed adaptive cubature information filters for multi-sensor systems with unknown probability of measurement loss

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