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The paper investigates the direction-of-arrival (DOA) estimation of narrowband signals with conventional co-prime arrays by using probabilistic Bayesianneural networks (PBNN). A super resolution DOA estimation method based onBayesian neural networks and a spatially overcomplete array output formulationovercomes the pre-assumption dependencies of the model-driven DOA estimationmethods. The proposed DOA estimation method utilizes a PBNN model to captureboth data and model uncertainty. The developed PBNN model is trained to do themapping from the pseudo-spectrum to the super resolution spectrum. Thislearning-based method enhances the generalization of untrained scenarios, andit provides robustness to non-ideal conditions, e.g., small angle separation,data scarcity, and imperfect arrays, etc. Simulation results demonstrate theloss curves of the PBNN model and deterministic model. Simulations are carriedout to validate the performance of PBNN model compared to a deterministic modelof conventional neural networks (CNN).

Direction-of-arrival estimation with conventional co-prime arrays using deep learning-based probablistic Bayesian neural networks