Ivan Milovanovic

Neural networks-based DOA estimation of multiple stochastic narrow-band EM sources

Localization of multiple stochastic narrow-band electromagnetic sources in the far-field is considered in the paper. Artificial neural networks-based approach is proposed to allow for an efficient direction of arrival (DOA) determination of electromagnetic signals radiated from stochastic sources as one of the key steps in the source localization procedure. It uses correlation matrix, obtained by signal sampling via antenna array in far-field scan area, to train an appropriate model based on MLP (Multi-Layer Perceptron) neural network. Proposed approach is validated on the example of a neural model performing accurate and fast one-dimensional (1D) DOA estimation of the position of three stochastic sources placed at fixed angle distance in azimuth plane.

Neural Network Approach for Efficient DOA Determination of Multiple Stochastic EM Sources in Far-field

An efficient approach for determination of incoming direction of electromagnetic (EM) signals radiated from multiple stochastic sources in far-field is presented in this paper. The approach is based on using a neural model realized by the Multi-Layer Perceptron (MLP) artificial neural network. MLP neural model, successfully trained by using correlation matrix of signals sampled by receiving antenna array, can be used to accurately determine a direction of arrival (DOA) of radiated EM signals and afterward a location of each of multiple stochastic sources in azimuth plane. Presented model is suitable for real-time applications as it performs fast the DOA estimation. The model architecture, results of its training and testing as well as simulation results are described in details in the paper.

Neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field

An efficient direction of arrival (DOA) estimation of multiple electromagnetic sources by using artificial neural network (ANN) approach is presented in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated and at arbitrary angular distance. The approach is based on training of the ANN in which the calculation of correlation matrix in the far-field scan area is done by using the Green function and the correlation of antenna elements feed currents used to describe stochastic sources radiation and then mapping this matrix to the space of DOA in angular coordinate. Once successfully trained, the neural network model is capable to perform an accurate DOA estimation within the training boundaries. Presented example verifies the accuracy of the proposed neural network model.

Estimation of the number of stochastic EM sources in far-field using Probabilistic Neural Network

In this paper, a neural model intended to efficiently determine the number of moving electromagnetic sources of stochastic radiation in the monitoring space sector is presented. Neural model is based on a probabilistic neural network. As an illustration, one-dimensional case is considered in which the noisy sources are moving only in the azimuth plane.

Efficient 2D localization of a number of mutually arbitrary positioned stochastic EM sources in far-field using neural model

In this paper, an architecture of a model for 2D space localization of sources of stochastic radiation is presented. Model represents the connection between planar antenna array for signal sampling and multilayer perceptron network for 2D Direction of Arrival (DOA) estimation. Proposed model is applied for the case of two stochastic sources that are moving independently in a plane parallel to a plane of antenna array.

Efficient DoA Tracking of Variable Number of Moving Stochastic EM Sources in Far-Field Using PNN-MLP Model

An efficient neural network-based approach for tracking of variable number of moving electromagnetic (EM) sources in far-field is proposed in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated, and at arbitrary angular distance. The neural network model is based on combination of probabilistic neural network (PNN) and the Multilayer Perceptron (MLP) networks and it performs real-time calculations in two stages, determining at first the number of moving sources present in an observed space sector in specific moments in time and then calculating their angular positions in azimuth plane. Once successfully trained, the neural network model is capable of performing an accurate and efficient direction of arrival (DoA) estimation within the training boundaries which is illustrated on the appropriate example.

Neural model for estimation of external noise power of receiver in wireless communication system

This paper presents MLP (Multilayer Perceptron Network) based neural models of external noise factor of wireless communication system receiver. The proposed models are trained by set of measured results from Recommendation ITU-R P.372. The noise factor calculated by proposed models is used in empirical formula to estimate external noise power. Due to great speed and acceptable accuracy, the estimation of external noise power of receiver by proposed models can be good alternative to manual reading from graphs and use of interpolation formulas.

Hybrid-empirical neural model for indoor/outdoor path loss calculation

Accurate prediction the coverage area provided by a given transmitting station is crucial to the efficient design of wireless communication systems. This paper presents hybrid-empirical neural model for indoor/outdoor loss calculation as alternative method to previous propagation models. Its primary advantage is the consideration many global and local parameters influencing the EM propagation. Further, it has great simulation speed that can give good base for introducing it in applications which simulation has to be finished in certain period of time.