Abstract: Aiming at the insufficient robustness problem of weak signal detection and modulation recognition in multi-sensor distributed reception systems, a new joint processing method based on deep learning is proposed. The proposed method adopts the distributed soft information fusion processing strategy where the signal detection and modulation recognition are integrated into a multi-variate hypothesis test problem. With the help of the excellent function approximation ability of DNN(Deep Neural Network), a method of joint pos terior probability solution and classification based on deep neural network DNN is proposed based on the analysis of network structure, objective function and network input and output. Finally, the performance of the proposed method is verified by simulation experiments, and compared with the existing methods. The results show that the proposed method can effectively fuse multiple sensor signals, and can significantly improve the classification accuracy with the increase of the number of receiving units. Compared to the existing confidence fusion methods based on equal weight combination, the proposed method has better performance, which is more obvious at low SNR(Signal-to-Noise Ratio) values, short signal lengths and large receiving units numbers.

Key words: signal detection; , modulation recognition; , multiple sensors; , distributed reception; , joint processing; , deep neural network(DNN)