Tomo-SAR imaging with sparse baselines can be formulated as a sparse signal recovery problem, which suggests the use of the Compressive Sensing (CS) method. In this paper, a novel Tomo-SAR imaging approach based on Sparse Bayesian Learning (SBL) is presented to obtain super-resolution in elevation direction and is validated by simulation results.

In this paper, we present a new frequency identification technique using the recent methodology of compressive sensing and discrete prolate spheroidal sequences with optimal energy concentration. Using the bandpass form of discrete prolate spheroidal sequences as basis matrix in compressive sensing, compressive frequency sensing algorithm is presented. Simulation results are given to present the effectiveness of the proposed technique for application to detection of carrier-frequency type signal and recognition of wideband signal in communication.