System Response Processing and HHT Method on Dynamic Specification Determination using Cloud Computation

Kelareh, Aryan Yousefyan; Shahri, Pouria Karimi; Khoshnevis, Seyed Alireza; Valikhani, Alireza

doi:10.1109/honet50430.2020.9322820

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…The Kalman filter uses the third-order Markov process to model the magnetic field according to (9), where 𝐵 is the magnetic field vector, and 𝑤 is the white Gaussian process noise. The use of the third-order Markov can estimate the first and second derivatives of the magnetic field vector and the field vector itself [11].…”

Section: System Simulation and Resultsmentioning

confidence: 99%

“…In the scenario where the spacecraft is fixed relative to the magnetic field vector (the measurement of the magnetic field vector does not change), the problem is completely invisible. Even in the steady-state spacecraft, condition and velocity have been estimated despite the longer convergence time [11]. Using two nested Kalman filters, the method presented in [11] can achieve better accuracy than the previous Kalman filter based on single magnetometer data.…”

Section: A a Review Of Attitude Estimation Methods Withmentioning

confidence: 99%

“…Even in the steady-state spacecraft, condition and velocity have been estimated despite the longer convergence time [11]. Using two nested Kalman filters, the method presented in [11] can achieve better accuracy than the previous Kalman filter based on single magnetometer data. The method that Nathanson and Challa first proposed is to use the finite difference to find the derivative of the magnetic field vector to provide the second measurement vector.…”

Section: A a Review Of Attitude Estimation Methods Withmentioning

confidence: 99%

“…It is necessary to extract the characteristic features to construct an accurate model of the system. In [11,13], they use empirical mode decomposition (EMD) and HHT by cloud computation to characterize a mechanical system. On the cloud space, the EMD method results in intrinsic mode functions (IMFs) of the structural acceleration responses, and the HHT calculations on the combination of these IMFs drive the value of natural frequency and system attenuation ratio.…”

Section: A Brief Introduction To Positioning Algorithmsmentioning

confidence: 99%

“…This method can only determine the attitude using the information of two sensors. Its purpose is to calculate the transmission matrix between the body device and the orbital device according to the information of the two sensors in the body device and the reference vectors corresponding to those sensors in the orbital device [13]. The problem with this method is that it cannot Carlos Silva, Extended Kalman Filter Design to Estimate the Attitude of the Nanosatellite System Using Magnetometer combine more than two sensors and does not provide an optimal method for determining the attitude.…”

Section: A Brief Introduction To Positioning Algorithmsmentioning

confidence: 99%

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Extended Kalman Filter Design to Estimate the Attitude of the Nanosatellite System Using Magnetometer

Silva,

McKell,

Parker

2023

Control Syst. Optim. Lett.

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The problem of estimating nonlinear systems is an important issue in many engineering applications. One of these applications is in the nanosatellite for Vehicle-to-everything (V2X) communications, which is used to replace sensors in the event of failure or error. Therefore, the accuracy of pre-filter estimation is of great importance. To control the orientation of a satellite, it is important to estimate the attitude accurately. Time-series estimation is especially important in micro and nanosatellites, whose sensors are usually low-cost and have higher noise levels than high-end sensors. Also, the algorithms should be able to run on systems with very restricted computer power. In this paper, an overview of the algorithms used to determine the attitude of nanosatellites, and especially using only the magnetometer sensor, will be discussed. This paper aims to simulate a nanosatellite system to estimate the magnetic field derivative vector using only the magnetometer data with the desired accuracy. The estimated vectors in the pre-filter, along with the magnetometer sensor data, are used to estimate the attitude of the satellite. The estimation of the state vector consisting of the vector part of quaternion and the angular velocity vector is used for the control calculation. To evaluate the accuracy of this pre-filter, a comparison of the magnetometer vector estimator with the body sensor data has been used.

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Section: System Simulation and Resultsmentioning

confidence: 99%

Section: A a Review Of Attitude Estimation Methods Withmentioning

confidence: 99%

Section: A a Review Of Attitude Estimation Methods Withmentioning

confidence: 99%

Section: A Brief Introduction To Positioning Algorithmsmentioning

confidence: 99%

Section: A Brief Introduction To Positioning Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Extended Kalman Filter Design to Estimate the Attitude of the Nanosatellite System Using Magnetometer

Silva,

McKell,

Parker

2023

Control Syst. Optim. Lett.

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Archives of Civil Engineering

Lu,

Dong,

Pan

et al. 2022

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Time series models have been used to extract damage features in the measured structural response. In order to better extract the sensitive features in the signal and detect structural damage, this paper proposes a damage identification method that combines empirical mode decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) models. EMD decomposes nonlinear and non-stationary signals into different intrinsic mode functions (IMFs) according to frequency. IMF reduces the complexity of the signal and makes it easier to extract damage-sensitive features (DSF). The ARIMA model is used to extract damage sensitive features in IMF signals. The damage sensitive characteristic value of each node is used to analyze the location and damage degree of the damaged structure of the bridge. Considering that there are usually multiple failures in the actual engineering structure, this paper focuses on analysing the location and damage degree of multi-damaged bridge structures. A 6-meter-long multi-destructive steel-whole vibration experiment proved the state of the method. Meanwhile, the other two damage identification methods are compared. The results demonstrate that the DSF can effectively identify the damage location of the structure, and the accuracy rate has increased by 22.98% and 18.4% on average respectively.

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System Response Processing and HHT Method on Dynamic Specification Determination using Cloud Computation

Cited by 2 publications

References 12 publications

Extended Kalman Filter Design to Estimate the Attitude of the Nanosatellite System Using Magnetometer

Extended Kalman Filter Design to Estimate the Attitude of the Nanosatellite System Using Magnetometer

Archives of Civil Engineering

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