Satellite microvibration measurement based on distributed compressed sensing

To reduce the burden of multiple sensors on satellites with limited resources, distributed compressed sensing (DCS) is applied to microvibration measurement. An improved joint recovery algorithm using the recovered spectrum coefficients as the halting condition is proposed. This improvement can address the drawback that the previous algorithms need to anticipate signal sparsity or use residuals that do not reflect recovery accuracy in the frequency domain as the halting condition. In addition, acceleration is adopted in the improved algorithm. The DCS-based microvibration measurement was conducted on a satellite mechanical test model. The results reveal that the DCS measurement has a difference in the order of 1e-10 g2/Hz between the power spectrum density (PSD) of the raw data and the recovery data. With one-fifth sampling points of the raw signal, it can achieve a signal difference of less than 4% in PSD, which demonstrates the viability of satellite microvibration measure-ment based on DCS.

Automated summary

In this study, distributed compressed sensing (DCS) was applied to microvibration measurement on satellites to reduce the burden of multiple sensors with limited resources. An improved joint recovery algorithm was proposed, achieving a signal difference of less than 4% in power spectrum density (PSD) with one-fifth sampling points of the raw signal. The results demonstrate the feasibility of satellite microvibration measurement based on DCS.