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.