Compressed Sensing Reconstruction Algorithm Based on Elman Neural Network

Compressed sensing theory, a approach in signal processing and sampling, transcends the constraints of the Nyquist sampling theorem, significantly lowering the costs associated with signal sampling. Signal reconstruction, a technique, strives to accurately recover the original signal from sub-sampling conditions. This study tackles the challenge posed by the necessity of prior knowledge about the sensing matrix in signal reconstruction, introducing an innovative compressed sensing blind reconstruction algorithm leveraging the Elman neural network. Diverging from traditional reconstruction methodologies, this neural network-based algorithm boasts adaptability and learning abilities, enabling it to discern signal characteristics and patterns directly from the observed data for effective signal reconstruction. Theoretical analysis and simulations affirm the algorithm’s superior performance in precisely reconstructing under-sampled signals, outperforming conventional algorithms in both accuracy and complexity.