Ricerc@Sapienza

In this paper, we consider the possibility to jointly exploit multiple frequency channels emitted by the same transmitter to improve target detection capability in a DVB-T based passive radar. In particular, appropriate multi-frequency techniques are presented for target detection to be effective in the considered application. The proposed approaches are validated and compared with reference to several experimental data for maritime surveillance applications.

In this paper we present a robust approach for target direction of arrival (DoA) estimation in passive radar that jointly exploits spatial and frequency diversity. Specifically we refer to a DVB-T based passive radar receiver equipped with a linear array of few antenna elements non-uniformly spaced in the horizontal dimension, able to collect multiple DVB-T channels simultaneously. We resort to a maximum likelihood (ML) approach to jointly exploit the target echoes collected across the antenna elements at multiple carrier frequencies.

This paper deals with the detection of small unmanned air vehicles (UAVs) and drones in high-density target scenarios such as airport terminal areas. In fact, in such conditions, strong reflections by high radar cross section (RCS) targets are likely to prevent the detection of very weak target echoes. In this work, we aim at overcoming this issue by taking advantage of long coherent processing intervals (CPIs) and by implementing a CLEAN-like multistage algorithm to remove the strongest target contributions.

In this paper, we consider the exploitation of a DVB-T based Passive Radar for simultaneous short and long range surveillance of drones and aircrafts, respectively. In detail, an airport terminal area surveillance application is considered and the effectiveness of the employed sensor is investigated with reference to the two search tasks to be accomplished simultaneously. For the purpose of our analysis, experimental data have been collected by means of the DVB-T based AULOS® passive sensor developed by Leonardo S.p.A against both a very small RCS drone and conventional air traffic.