Satellite microvibration measurement based on distributed compressed sensing

Li, Li; Zhou, Miaomiao; Zhu, Ye; Dai, Ya; Liang, Xuwen

doi:10.1016/j.measurement.2022.112031

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…PSD is frequently used to assess the satellite microvibrations [11]; therefore, the step from recovery solution s to recovery signal x can be omitted in the microvibration measurements, and the PSD of the signal can be obtained directly from sparse signal s as:…”

Section: Microvibration Measurement Using Dynamic Csmentioning

confidence: 99%

“…Microvibrations are primarily caused by moving parts on the satellite, such as reaction wheels and thrusters, and disturbances in the space environment [9,10]. It is challenging to simulate all space disturbances on the ground [11]; thus, inorbit measurements are needed for an accurate assessment of microvibrations. A high sampling rate is required in orbit to collect precise data across a wide spectrum.…”

Section: Introductionmentioning

confidence: 99%

“…However, complex compression algorithms introduce an additional computational burden for the satellite. Another solution to this problem is compressed sensing (CS), which has been used in satellite microvibration measurements [11] and microvibration separation [15]. The signal is collected on the satellite with significantly fewer samples and then recovered on the ground.…”

Section: Introductionmentioning

confidence: 99%

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Microvibration streaming measurements using dynamic compressed sensing for satellites

Zhou

Zhu

et al. 2023

Meas. Sci. Technol.

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Long-term monitoring of satellite microvibrations generates a significant amount of data streams, placing strain on satellites with limited transmission capacity. To relieve this transmission strain, a dynamic compressed sensing (CS) framework is proposed for satellite microvibration measurements. Microvibration streams are measured block by block and then reconstructed by a dynamic recovery algorithm. The recovery solution of one block can be used as a priori knowledge for the next block, allowing for faster updates. However, the existing dynamic recovery algorithms are only applicable to the real domain and cannot be applied to the microvibrations projected on a Fourier basis in the complex domain. In light of this, the dynamic homotopy algorithm is expanded to the complex domain to deal with microvibration signals that are sparse on the Fourier basis. In comparison to the traditional uniform sampling methods, the experimental results show that the dynamic CS with the expanded recovery algorithm can achieve a maximum root-mean-square acceleration (Grms) deviation of 4% in power spectrum density (PSD) with 1/5 sampling points. Compared with recovery algorithms applicable to fixed measurements, the dynamic algorithm can achieve comparable accuracy in about 1/3 of computation time. The experimental results demonstrate the feasibility of satellite microvibrations measurement using dynamic CS.

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Section: Microvibration Measurement Using Dynamic Csmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%