In this manuscript we investigate the implications of adopting a double cyclic prefix in the orthogonal timefrequency space modulation. Our study first focuses on the analysis of the modulated signal and on the development of a useful model for the received signal in the presence of a doubly selective fading channel. On the one hand, our mathematical results allow us to accurately assess the impact of pulse shaping on the structure of the transmitted waveform and on its power spectral density, and to develop some simple rules for allocating multiple pilot symbols within each orthogonal time-frequency space symbol. On the other hand, they are exploited to develop a novel algorithm for pilot-aided channel estimation, whose output provides a detailed representation of the communication channel; for this reason, it can be used for sensing at the transmit side in integrated sensing and communication applications, or for channel equalization at the receive side in digital communications. Our numerical results evidence that our channel estimation & equalization algorithm outperforms the other related techniques available in the technical literature at the price of a limited increase in computational complexity.

In this manuscript, an innovative method for the detection and the estimation of multiple targets in a radar system employing orthogonal frequency division multiplexing is illustrated. The core of this method is represented by a novel algorithm for the detection of multiple superimposed two-dimensional complex tones in the presence of noise and the for estimation of their parameters. This algorithm is based on a maximum likelihood approach and combines a single tone estimator with a serial cancellation procedure. Our numerical results lead to the conclusion that the developed method can achieve a substantially better accuracy-complexity trade-off than various related techniques in the presence of closely spaced targets.

A document by Giacomo Paterniani . Click on the document to view its contents.

In this manuscript, novel methods for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived, and their application to colocated multiple-input multiple-output radar systems is investigated.

Radars are expected to become the main sensors in various civilian applications, ranging from health-care monitoring to autonomous driving. Their success is mainly due to the availability of both low cost integrated devices, equipped with compact antenna arrays, and computationally efficient signal processing techniques. An increasingly important role in the field of radar signal processing is played by machine learning and deep learning techniques. Their use has been first taken into consideration in human gesture and motion recognition, and in various healthcare applications. More recently, their exploitation in object detection and localization has been also investigated. The research work accomplished in these areas has raised various technical problems that need to be carefully addressed before adopting the above mentioned techniques in real world radar systems. In this manuscript, a comprehensive overview of the machine learning and deep learning techniques currently being considered for their use in radar systems is provided. Moreover, some relevant open problems and current trends in this research area are analysed. Finally, various numerical results, based on both synthetically generated and experimental datasets, and referring to two different applications are illustrated. These allow readers to assess the efficacy of specific methods and to compare them in terms of accuracy and computational effort.