Sparsity Adaptive CoSaMP Based on Dynamic Threshold and Weak Atom Selection for Underwater Acoustic Sparse Channel Estimation

The compressive sampling matching pursuit (CoSaMP) algorithm is widely used in orthogonal frequency division multiplexing (OFDM) underwater acoustic (UWA) sparse channel estimation with good estimation accuracy. However, the algorithm requires channel sparsity as a priori information, and the large delay of the UWA channel leads to a large number of columns in the measurement matrix, which greatly increases the computational complexity of CoSaMP. To address these issues, a sparsity adaptive CoSaMP algorithm based on dynamic threshold and weak selection of atoms (DW-SACoSaMP) is proposed in this paper. The algorithm can quickly estimate the channel impulse response (CIR) without prior knowledge of channel sparsity when applied to UWA sparse channel estimation. Simulation results show that the proposed algorithm achieves higher estimation accuracy and lower computational complexity compared with other mainstream reconstruction algorithms, and its advantage in estimation accuracy becomes more significant when the signal-to-noise ratio (SNR) is greater than 8 dB.

IEEE Wireless Communications Letters