2 research outputs found
Direction-of-arrival estimation with conventional co-prime arrays using deep learning-based probablistic Bayesian neural networks
The paper investigates the direction-of-arrival (DOA) estimation of narrow
band signals with conventional co-prime arrays by using probabilistic Bayesian
neural networks (PBNN). A super resolution DOA estimation method based on
Bayesian neural networks and a spatially overcomplete array output formulation
overcomes the pre-assumption dependencies of the model-driven DOA estimation
methods. The proposed DOA estimation method utilizes a PBNN model to capture
both data and model uncertainty. The developed PBNN model is trained to do the
mapping from the pseudo-spectrum to the super resolution spectrum. This
learning-based method enhances the generalization of untrained scenarios, and
it provides robustness to non-ideal conditions, e.g., small angle separation,
data scarcity, and imperfect arrays, etc. Simulation results demonstrate the
loss curves of the PBNN model and deterministic model. Simulations are carried
out to validate the performance of PBNN model compared to a deterministic model
of conventional neural networks (CNN).Comment: 7-page