A Deep Learning-Based Algorithm for Multi-Feature Processing and Signal Detection

In the field of signal detection, traditional signal processing algorithms encounter challenges in effectively integrating multiple features for joint detection. To address this issue, we propose a multi-feature fusion detection algorithm. This algorithm processes various signal features, including matched filtering results, Short-Time Fourier Transform (STFT) features, and wavelet transform features. For the matched filtering results, we employ a Long Short-Term Memory (LSTM) network, while a convolutional neural network (CNN) is utilized for processing time-frequency domain features. After processing multiple features and performing a weighted summation of the results, the algorithm yields the final detection outcome. Simulation results indicate that the algorithm demonstrates a high level of detection accuracy.