Robust Kalman filter for systems subject to parametric uncertainties

Rocha, Kaio D. T.; Terra, Marco H.

doi:10.1016/j.sysconle.2021.105034

Cited by 17 publications

(7 citation statements)

References 18 publications

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“…In recent research, robust and adaptive solutions are proposed to resolve the lack of robustness issue in the KF estimator of the integrated navigation systems (13)(14)(15). However, these methods assume predetermined constraints on the signal uncertainty model and also add some complexity to the signal processing algorithms of the estimation procedure.…”

Section: Related Workmentioning

confidence: 99%

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

Ansari,

Jamali,

Fatehi-Dindarlou

et al. 2024

IJE

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Implementing a proper integration scheme plays an important role in the performance of integrated navigation systems. Not only does employing a more reliable estimation method improve the accuracy of the integrated navigation system, but this can lead to a more robust solution in the presence of different types of uncertainties. Implementing an integration scheme that has a robust and simple structure is a challenging issue in the design of integrated navigation systems. By inspiring from the concept of PID control, this paper proposes a robust integration scheme for aided inertial navigation systems in the presence of aiding sensor measurement uncertainties. The proposed filter combines the concept of proportional-integral-derivative control theory and the standard Kalman filter estimator to improve the performance of the integration scheme. Thanks to the integral and derivative parts added to the proposed scheme, the integrated system attains a faster and more robust solution in the presence of observation errors and uncertainties. The simulation case studies validate the superior efficacy and capability of the proposed scheme compared to the integration method based on the standard Kalman filter.

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Section: Related Workmentioning

confidence: 99%

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

Ansari,

Jamali,

Fatehi-Dindarlou

et al. 2024

IJE

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“…Aiming at the error problem between the rainfall estimation result obtained by ordinary Kalman filter and the actual precipitation, we decide to improve on the basis of the original Kalman filter calibration radar combined with rain gauge rainfall estimation model [16].…”

Section: Improve Rainfall Estimation Model 31 Improved Rainfall Estim...mentioning

confidence: 99%

Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm

Zhang¹,

Fang²,

Jia³

et al. 2022

Journal of Quantum Computing

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In order to solve the rainfall estimation error caused by various noise factors such as clutter, super refraction, and raindrops during the detection process of Doppler weather radar. This paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman filter. After data preprocessing, the radar data should be classified according to the precipitation intensity. And then, they are respectively substituted into the improved filter for calibration. The state noise variance Q (k) and the measurement noise variance R (k) can be adaptively calculated and updated according to the input observation data during this process. Then the optimal parameter value of each type of precipitation intensity can be obtained. The state noise variance Q (k) and the measurement noise variance R (k) could be assigned optimal values when filtering the remaining data. This rainfall estimation based on semiadaptive Kalman filter calibration not only improves the accuracy of rainfall estimation, but also greatly reduces the amount of calculation. It avoids errors caused by repeated calculations, and improves the efficiency of the rainfall estimation at the same time.

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“…As another way to deal with model uncertainty, H ∞ KF minimizes the H ∞ norm of estimation error in the worst-case scenario without making any assumptions about model uncertainty and noise statistics [58][59][60]. This method is specifically designed for robustness, but it is hard to guarantee that the H ∞ performance parameter γ will satisfy the filter existence conditions at every step [28,61]. With the popularization of deep learning, using DNN to assist KF in dealing with model uncertainty becomes a new solution [62][63][64][65][66].…”

Section: Introductionmentioning

confidence: 99%

A generalized data assimilation architecture of digital twin for complex process industrial systems

Zhao,

Jiang,

Cai

et al. 2024

Meas. Sci. Technol.

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As one of the critical cores of digital twin (DT), data assimilation (DA) can maintain consistency and synchronization between DT and physical system. Kalman filtering is a common DA method, but its estimation performance is deteriorated by factors such as model inaccuracy and time-varying noise covariance in practical applications. The errors caused by these multiple uncertainties are all coupled in the measurements, which augments the difficulty for DT to obtain physical system information. In order to tackle the DA problem with multiple uncertainties, this paper proposes a generalized DA architecture of DT for sophisticated process industry. First, combining Stein variational gradient descent (SVGD) and nonlinear Bayesian filtering paradigm, a recursive estimation framework is established, which has higher accuracy in estimating the noise covariance compared to traditional methods. Second, to effectively deal with model inaccuracy by using the filtering residual containing time-varying noise, we propose a neural network and modified wavelet-based model error compensation (NNMW-MEC) block. Based on modified wavelet technique, the filtering residual denoising (FRD) built in NNMW-MEC can better cope with time-varying noise compared to existing wavelets, and extract the low-frequency signal involving model error information from noisy residual smoothly. In addition, because of the neural network-based state-compensation (NNSC) subblock, NNMW-MEC has more outstanding ability in compensating the state deviations with large changing range. Finally, we take the boiler system in a coal-fired power plant as an example to verify the effectiveness of our architecture. Experimental results show that the DA architecture proposed in this paper can improve the estimation performance of DT under inaccurate model and uncertain noise statistics.

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Robust Kalman filter for systems subject to parametric uncertainties

Cited by 17 publications

References 18 publications

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

A Novel Sensor Integration Scheme for an Aided Inertial Navigation System Based on a Generalized PID Filter in the Presence of Observation Uncertainty

Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm

A generalized data assimilation architecture of digital twin for complex process industrial systems

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