A time-varying Kalman filter for low-acceleration attitude estimation

Díaz, Álvaro Deibe; Nacimiento, José A. Antón; Cardenal, Jesús; Peña, Fernando López

doi:10.1016/j.measurement.2023.112729

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…The integration of machine learning into virtual sensing not only enables the estimation of unmeasured variables, but also empowers decision-making processes in various sectors, such as healthcare, manufacturing, and environmental monitoring, resulting in a significant transformation in how we address sensing limitations [ 10 , 11 , 12 ]; In the deterministic approach, the physical or chemical connections between input and output variables are leveraged to calculate the unmeasured variable through a virtual sensor [ 13 ]. Usually, virtual sensing based on the deterministic approach is performed using methodologies based on the Kalman filter due to its ability to combine available data with system dynamics to estimate unmeasured variables [ 14 , 15 , 16 , 17 ]. Its widespread application across various sectors such as autonomous systems, finance, and environmental monitoring highlights its significance in addressing complex problems where direct measurements are unattainable.…”

Section: Introductionmentioning

confidence: 99%

“…In the deterministic approach, the physical or chemical connections between input and output variables are leveraged to calculate the unmeasured variable through a virtual sensor [ 13 ]. Usually, virtual sensing based on the deterministic approach is performed using methodologies based on the Kalman filter due to its ability to combine available data with system dynamics to estimate unmeasured variables [ 14 , 15 , 16 , 17 ]. Its widespread application across various sectors such as autonomous systems, finance, and environmental monitoring highlights its significance in addressing complex problems where direct measurements are unattainable.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Virtual Sensing for Electrochemical Sensors

Sangiorgi,

Sberveglieri,

Carnevale

et al. 2024

Sensors

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In recent years, the application of machine learning for virtual sensing has revolutionized the monitoring and management of information. In particular, electrochemical sensors generate large amounts of data, allowing the application of complex machine learning/AI models able to (1) reproduce the measured data and (2) predict and manage faults in the measuring sensor. In this work, data-driven models based on an autoregressive model and an artificial neural network have been identified and used to (i) evaluate sensor redundancy and (ii) predict and manage faults in the context of electrochemical sensors for the measurement of ethanol. The approach shows encouraging results in terms of both performance and sensitivity analyses, allowing for the reconstruction of the values measured by two sensors in a series of six sensors with different dopant levels and to reproduce their values after a fault.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Virtual Sensing for Electrochemical Sensors

Sangiorgi,

Sberveglieri,

Carnevale

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Non-destructive detection of sturgeon breath under waterless low temperature stress using microenvironment and breath angle multi-modal sensing

Zhang,

Li,

Wang

et al. 2024

Biosystems Engineering

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An improved attitude estimation algorithm for suppressing magnetic vector disturbance based on extended Kalman filter

Zong,

Su,

Gao

et al. 2024

Meas. Sci. Technol.

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This paper proposes an improved attitude estimation algorithm based on the extended Kalman filter (EKF), and it is applied to suppress the accuracy reduction in attitude estimation caused by fusing magnetometer data under large angular motion. In the proposed attitude estimation structure, the approximate variance of the estimated horizontal northbound magnetic vector is used to dynamically adjust the participation of magnetometer data in attitude estimation, as the approximate variance increases significantly under large angular motion and fusing magnetometer data will reduce estimation accuracy. A three-axis position-velocity controlled turntable is used to conduct rocking experiments for validating the proposed attitude estimation algorithm. The results show a significant improvement in yaw angle estimation accuracy with the proposed attitude estimation algorithm and correspondingly enhance the distribution of pitch and roll angle errors.

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A time-varying Kalman filter for low-acceleration attitude estimation

Cited by 5 publications

References 32 publications

Data-Driven Virtual Sensing for Electrochemical Sensors

Data-Driven Virtual Sensing for Electrochemical Sensors

Non-destructive detection of sturgeon breath under waterless low temperature stress using microenvironment and breath angle multi-modal sensing

An improved attitude estimation algorithm for suppressing magnetic vector disturbance based on extended Kalman filter

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