Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization

Abstract

In order to reduce the sea clutter interference in the detection of sea surface targets, we propose a bistatic sea clutter suppression method based on compressed sensing optimization in this paper. The proposed method mitigates the interference effect by reconstructing and cancelling out the sea clutter. Since the fixed sparse base is not always completely applicable for the sparse representation of sea clutter, we propose a sparse base optimization algorithm based on transfer learning to convert the fixed sparse base into an adaptive one. Moreover, we introduce a sensing matrix optimization algorithm to decrease the cross-correlation coefficient between the measurement matrix and the sparse base matrix, which can enhance the signal reconstruction quality. Finally, we use the orthogonal matching pursuit algorithm to reconstruct the sea clutter and employ the reconstructed results to cancel and suppress the sea clutter. The simulation results demonstrate that the proposed method outperforms the traditional methods such as the root time-domain cancellation method and the singular value decomposition method (SVD). Therefore, the proposed method has great practical significance for the detection of bistatic sea surface targets.

Principle and Recent Development in Photonic Time-Stretch Imaging

“…The RPBSs mixed the pulse signal when the light pulses propagated through the Mach-Zehnder modulator (MZM). The light pulses , The second technique is named compressive sensing (CS) [16][17][18]73,[99][100][101][102]. Due to the sparsity of the desired imaging, the extensively used CS method can reduce the number of measurements and offers a high-efficient data acquisition process.…”