LAUSR

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 measuring satellite microvibrations. Microvibration streams are measured block by block and then reconstructed using 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, existing dynamic recovery algorithms are only applicable in the real domain and cannot be applied to microvibrations projected on a Fourier basis in the complex domain. In light of this event, the dynamic homotopy algorithm is expanded to the complex domain to deal with microvibration signals that are sparse in the Fourier basis. In comparison to conventional 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 with one-fifth of the sampling points. Compared to recovery algorithms applicable to fixed measurements, the dynamic algorithm can achieve comparable accuracy in about one-third of the computation time. The experimental findings demonstrate the feasibility of satellite microvibrations measurements using dynamic CS.