Dottorato in TECNOLOGIE DELL'INFORMAZIONE E DELLE COMUNICAZIONI (ICT)

Titolo della tesi: ADVANCED ISAR TECHNIQUES WITH MONOSTATIC AND MULTISTATIC RADAR SYSTEMS

Inverse synthetic aperture radar (ISAR) is an imaging technique that uses the motion of a target to obtain a high-resolution radar image of it. The radar images obtained by this technique are commonly used to recognize unknown detected targets when cooperative identification is not possible. The main novelties that this research has led to can be identified in two main areas, one concentrating the combination of Synthetic Aperture Radar (SAR) and ISAR techniques to extract detailed moving maritime targets characteristics in monostatic systems, the other regarding the estimation of kinematic target parameters in multistatic ISAR systems. In the first part of this thesis, we have proposed a comprehensive processing chain to support maritime situational awareness, addressing the effective understanding of the global maritime domain. The processing chain is composed of interconnected blocks that incorporate a combination of established techniques from literature and novel approaches. These blocks collectively generate a comprehensive set of target information, including focused images, motion parameter data and accurate position coordinates. The proposed processing chain was extensively evaluated on a dataset of 14 Single Look Complex (SLC) SAR data obtained from COSMO-SkyMed (CSK) and Cosmo Second generation (CSG) constellations. The effectiveness of the proposed approach is quantitatively assessed exploiting the Automatic Identification System (AIS) information as the reference ground truth. In the second part of this thesis, we address the problem of translational motion estimation in multistatic ISAR systems operating with an active radar sensor and multiple receiving only devices spatially separated. Specifically, we proposed a two-step decentralized technique: the first step estimates specific signal parameters (i.e., Doppler frequency and Doppler rate) at the single sensor level while the second step exploits these estimated parameters to derive the target velocity and acceleration components. A closed form theoretical performance analysis is provided for the overall technique, which is then used to assess the achievable performance against angular diversity among the sensors. Additionally, a comparison with a state-of-the-art centralized approach has been carried out considering computational burden and robustness. Finally, results obtained against experimental multisensor data are shown confirming the effectiveness of the proposed technique and supporting its practical application.