A deterministic approach to the synthesis of a general aperiodic arc or ring antenna array is presented and thoroughly detailed in Part I of this work. The proposed technique, which is based on the auxiliary array pattern concept, is aimed at the semi-analytical determination of the optimal excitation tapering and array element angular distribution for mimicking a given radiation pattern mask, without resorting to any optimization procedure. This, in turn, enables a dramatic reduction in design times. The developed technique is validated by application to the synthesis of antenna arrays featuring different radiation pattern masks typically adopted in satellite communications and radar applications; the relevant results are reported in Part II.

The aim of Part II of this work is to illustrate the application of the antenna placement methodology introduced in Part I for the deterministic synthesis of general conformal aperiodic arrays subject to multiple concurrent design constraints on the relevant layout and excitation tapering, as it is typically the case in satellite communications and radar applications. Antenna mutual coupling is not considered in the developed methodology; hence, to assess the sensitivity of the design procedure to such non-ideality, a dedicated investigation has been conducted using rigorous full-wave electromagnetic modeling and experimental measurements collected on physical prototypes. The obtained results demonstrate the effectiveness and versatility of the proposed array synthesis approach, even in operative scenarios in which a significant deviation from the desired antenna operation is observed.

The article presents the results of research into a method for representing complex data based on an overcomplete basis and l₀/l₁ norms. The proposed method is an extended modification of the neural-like MFNN (minimum fuel neural network) for the case of complex data. The influence of the choice of activation function on the performance of the method is analyzed. The results of the numerical simulation demonstrate the effectiveness of the proposed method for the case of sparse representation of complex data and can be used to determine the direction of arrival (DOA) for a uniform linear array (ULA).

This article outlines the results of comparison methods for representing complex data based on a redundant basis using the L0 norm and analyses the method of a modified MFNN (minimum fuel neural network) and the sparse representation method for the complex-data SL0 (smoothed L0 norm), based on the smoothed L0 norm. The example of numerical modeling for determining the direction of arrival (DOA) of sources received by an equidistant antenna array (ULA—Uniform Linear Array) shows that the SL0 method ensures a high convergence rate. However, unlike the MFNN-like neural network method, it does not guarantee convergence to the correct solution.

BACKGROUND: Recognition and representation of the road scene is a relevant task in the field of autonomous driving. One of the ways to improve the characteristics of the existing sensory hardware is the use of neural networks for signal processing. AIM: To conduct an experimental study of the possibilities of using the modified MFNN neural network to increase the resolution of a radar with a small number of channels, to compare it with the classical algorithm based on the fast Fourier transform (FFT), and, in addition, to compare the results with the data obtained from other types of sensors (lidars). METHODS: The algorithm of the road scene representation, in particular, the detection of pedestrians and cars, is used with a millimeter-range automotive radar and a method for determining data components in relation to the DOA problem, based on the MFNN neural network, modified for the case of representing signals taking complex values in the form of excessive basis coefficients minimizing these coefficients according to the L1 norm. The algorithm based on the fast Fourier transform (FFT) is used for comparative analysis. RESULTS: As a result of the conducted research, confirmation of the practical feasibility of the developed modifications of the MFNN method was obtained, and the advantage of using a neural network, consisting in increasing the degree of detail of objects, the accuracy of determining their shape and position using a radar with a small number of channels, was demonstrated. CONCLUSION: The obtained results can be used to develop solutions to improve the efficiency of detecting obstacles on the way of transport, automatic vehicle control, continuous environmental monitoring, and so on, which helps to improve the safety and efficiency of highly automated and autonomous systems.