Direction of arrival estimation of mobile stochastic electromagnetic sources with variable radiation powers using hierarchical neural model

Hierarchical neural model for an efficient one‐dimensional direction of arrival (DoA) estimation of stochastic electromagnetic (EM) sources with a variable radiation power is proposed. Model is trained to provide an azimuth position of such sources based on a spatial correlation matrix obtained by a signal sampling at a reception point and then used as an input to a neural model. It consists of two hierarchical levels realized by the multilayer perceptron (MLP)‐based neural networks. The first level is responsible to reduce the dimensionality of the considered DoA problem which allows for its easier solution at the second level. Accuracy and run‐time of the proposed model is verified on an example of determining the azimuth position of two stochastic EM sources in noisy conditions through comparison with a standalone MLP model and a model based on the root MUSIC algorithm.