Multi-sensor Joint Signal Detection Method Based on Deep Neural Network

‍ ‍Aiming at the problem of signal detection in multi-sensor distributed reception， a joint detection method under the minimum error probability criterion is proposed. The proposed method adopts the distributed soft information fusion processing strategy， and regards multi-sensor signal detection as binary hypothesis test. With the help of the excellent function approximation ability of deep neural network（DNN）， based on the analysis of neural network structure， objective function and network input and output， a posteriori probability solution method of hypothesis test based on DNN is given. Each independent receiving unit uses the DNN to estimate the posterior probability of the signal with or without two assumptions， and then sends it to the fusion center to calculate the joint posterior probability distribution and make a decision. Different from the strict mathematical derivation of the traditional processing process， the parameter analysis and feature extraction of the proposed method do not need to be solved manually. Finally， the effectiveness 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. With the increase of the number of receiving units， the signal detection probability can be significantly improved and the false alarm probability can be reduced. Compared with the current typical S/ K fusion methods， the proposed method has obvious advantages at low signal-to-noise ratio （SNR） values.