Sparsity Order Estimation for Compressed Sensing System using sparse Binary Sensing Matrix

We present a composite Compressed Sensing system for the acquisition and recovery of compressible signals, where sparse Binary Matrix aids Sparsity Order Estimation, Gaussian reconstruction. The is deterministic adapted according to varying nature sparsity order. estimate order by exploiting structure statistics obtained measurements. refine estimates using Kalman filter with discrete Markov model that characterizes variation. A Matrix-Aided Orthogonal Matching Pursuit developed faster signals. Simulation results on real-world synthetic data demonstrate merits proposed estimation methods compared other existing methods. Our are practical recover signals at least 25% than