Abstract-In this paper, we present an efficient Artificial Neural Network (ANN)-based model to estimate both azimuth and elevation arrival angles of a signal source. To achieve this goal, the ANN model is constructed using measurement data obtained by a rectangular antenna array in the space-frequency domain. Unlike classical superresolution algorithms such as 2D MUSIC, the proposed model is capable to account for imperfections of measurement equipment as well as mutual couplings between array elements. The neural model has been verified for several angular positions and frequencies. It is shown that the use of ANN model to estimate angular positions of a signal source yields more accurate results when compared to 2D MUSIC. Moreover, the neural model significantly outperforms 2D MUSIC in terms of speed of computation.

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In order to provide a constant and complete operational picture of the maritime situation in the Exclusive Economic Zone (EEZ) at over the horizon (OTH) distances, a network of high frequency surface-wave-radars (HFSWR) slowly becomes a necessity. Since each HFSWR in the network tracks all the targets it detects independently of other radars in the network, there will be situations where multiple tracks are formed for a single vessel. The algorithm proposed in this paper utilizes radar tracks obtained from individual HFSWRs which are already processed by the multi-target tracking algorithm at the single radar level, and fuses them into a unique data stream. In this way, the data obtained from multiple HFSWRs originating from the very same target are weighted and combined into a single track. Moreover, the weighting approach significantly reduces inaccuracy. The algorithm is designed, implemented, and tested in a real working environment. The testing environment is located in the Gulf of Guinea and includes a network of two HFSWRs. In order to validate the algorithm outputs, the position of the vessels was calculated by the algorithm and compared with the positions obtained from several coastal sites, with LAIS receivers and SAIS data provided by a SAIS provider.

The approximation problem of a filter function of even and odd order is solved mathematically in this paper most directly applying the proposed Christoffel-Darboux formula for two continual orthogonal polynomials on the equal finite segment. As a result, a linear phase low-pass digital finite impulse response (FIR) filter function is generated in compact explicit form. In addition, a new difference equation and structure of digital FIR filter are proposed. Two examples of the extremely economic FIR filters (with four adders and without multipliers) designed by the proposed technique are presented. The proposed solutions are efficient in regard to energy consumption and have a high selectivity.

This article describes an implementation of a compact wire model into the three-dimensional transmission-line matrix (TLM) cylindrical mesh for the purpose of an efficient analysis of probe-coupled cylindrical microwave cavity devices. Because of a cylindrical grid structure and empirical nature of the compact model, this implementation has to take into account a change of wire model parameters with a variable cross section of the TLM nodes through which a wire conductor passes. The model accuracy has been experimentally verified and compared with the corresponding results reached by the TLM method based on a rectangular grid in order to consider its advantages.

MLP_DoA module, being an integral part of the smart TWAA DoA subsystem, intended for fast DoA estimation is proposed. Multilayer perceptron network is used to create the MLP_DoA module that provides a radio gateway location in azimuthal plane at its output when a spatial correlation matrix, found by receiving the radio gateway signal using two-element textile wearable antenna array, is on its input. MLP_DoA network training with monitoring the generalization capabilities on the validation set of samples is applied. The accuracy of the proposed modeling approach is compared to the classical approach in MLP_DoA module training previously developed by the authors. Comparison of the presented ANN model with the root MUSIC algorithm in terms of accuracy and program execution time is also done.

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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.