Radiation source location technology of complex space based on point cloud neural network

This paper presents a point cloud neural network (PCNN) to locate radiation sources in complex space. The network has the ability to process 3D sampling information which consists of the electric field intensity and coordinate. The ability of the network to predict the location of the radiation source is obtained by training and fitting the relationship between the sampled electromagnetic information and the radiation source position. Compared with traditional methods, the proposed PCNN method avoids the calculation of electromagnetic wave propagation law in complex space, as well as reduce the computing resources and time required for positioning process. The performance of the proposed method is validated by a series of simulation dataset.