Adaptive Channelized Greedy Algorithm for Analog Signal Compressive Sensing

In the development of analog signal compressive sensing (CS), the degradation of reconstruction performance under noise is the main bottleneck because the CS framework is very sensitive to noise. This paper proposes an adaptive channelization-based orthogonal matching pursuit algorithm (C-OMP) combining the channelization and the adaptive iteration methods. The proposed C-OMP has two steps: channel screening and global iteration. Based on the proposed method, the original signal can be recovered adaptively in high probability of success with fewer observations under the noise background. Simultaneously, the noise can be reduced as much as possible to enhance the output signal-to-noise ratio (SNR) by excluding the noise channel during the channel screening and separating noise atoms during the global iteration. The relationship between the probability of successful reconstruction and the number of observations is mathematically analyzed. Furthermore, the parameter settings, computational complexity, and output SNR are analytically evaluated. The simulation results confirm the analytical results and further demonstrate the effectiveness and advantages of the C-OMP in the noise environment. Overall, the proposed algorithm considerably improves the performance of the analog signal CS in the practical noisy environment.