Compressed Sensing Using Adaptive Sparse Measurements
نویسندگان
چکیده
Compressed sensing (CS) using sparse measurement matrices and iterative messagepassing reconstruction algorithms have been recently investigated as a low-complexity alternative to traditional CS methods. In this paper, we investigate the adaptive version of well-known Sudocodes scheme, where the sparse measurement matrix is progressively created based on the outcomes of previous measurements. Inspired by resemblance with rateless coding, we provide a detailed analysis of the adaptive Sudocodes approach in combination with the verification-based LM1 reconstruction. The results show that the adaptivity is a promising feature for reducing complexity and improving performance of CS methods based on sparse measurement matrices.
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