A cloud-edge collaborative deep network for signal compression and reconstruction in aerospace testing

Abstract To address the real-time processing requirements of massive multi-source signals in aerospace product integrated testing, this paper proposes a cloud-edge collaborative signal compression and reconstruction method based on a deep compressed sensing network. Targeting the transmission bottlenecks in cloud-edge architectures and the fragmentation of temporal signal dependencies, a dual-stage optimization method is developed: (1) At the edge side, a dual-branch convolutional compression network is designed to achieve adaptive compression of multi-form signals through global feature observation and local attention enhancement. (2) On the cloud side, a bidirectional LSTM (BiLSTM) combined with a progressive stacking structure is employed to establish a cross-temporal signal correlation reconstruction mechanism. The proposed method is evaluated on both public dataset (500 Hz, 12-channel, n = 600) and real-world dataset (1000 Hz, 190k points/signal, n = 396). Experimental results demonstrate superior performance over traditional compressed sensing and deep learning methods, achieving lower reconstruction errors while maintaining high compression rates, thereby effectively balancing the trade-off between compression efficiency and reconstruction fidelity.