Signal Compression Sensing Algorithm Based on Inception-LSTM

To utilize resources effectively and improve transmission efficiency, signal compression sensing technology is widely used. Compared with traditional signal compression sensing algorithms, neural network-based compression sensing algorithms have attracted more attention due to their excellent performance. However, the existing compressed sensing methods do not perform well in the face of complex one-dimensional data. In contrast, this paper proposes a signal-compressed sensing algorithm based on inception-LSTM. The algorithm consists of four parts, including signal compression, signal processing, preliminary reconstruction, and signal secondary reconstruction. By using the improved inception module and the LSTM model, the signal is processed during the initial reconstruction and further reconstruction stages to improve the quality and accuracy of the signal. Experimental results show that the proposed method achieves good results in signal compression and reconstruction.