Titolo della tesi: EXPLOITATION OF ANGULAR DIVERSITIES IN EMERGING MULTISTATIC RADAR SENSORS - APPLICATIONS AND METHODS TO GNSS-BASED PCL AND DRONE-BORNE SAR SYSTEMS
In the pursuit of advanced radar solutions, the integration of data from diverse radar modes has emerged as pivotal. Angular diversity, in particular, enables the capture of target information from multiple perspectives, surmounting the constraints of single-perspective radar and leading to a more precise understanding of targets and their surroundings. This diversity is usually realized through multi-sensor schemes, employing widely separated transmitting and receiving radar nodes, allowing for exploration of varied target-sensor geometries and collection of backscattered energy from targets from different angles. This not only enhances our understanding of target structures but also mitigates challenges posed by targets’ radar-cross-section scintillation.
This thesis explores the integration of multiple acquisitions to address the growing demand for innovative radar solutions. It centers on two multi-sensor schemes harnessing angular diversity: GNSS-based multistatic passive radar systems and high-resolution drone-borne SAR systems utilizing multiple platforms. GNSS-based passive radar benefits from the simultaneous illumination of the target area by multiple satellites, while the versatility of deploying drones allows for diverse platform configurations, yielding a comprehensive dataset. This significantly enhances system capabilities, enabling advanced functionalities such as target detection, localization, kinematic parameter estimation, imaging, and feature extraction.
By leveraging GNSS satellites for passive radar systems, we propose a novel approach for target detection and localization that maximizes the benefits of angular diversity offered by navigation satellites. This approach operates entirely on the Cartesian plane, providing simultaneous detection and localization of targets in the surveyed area. Additionally, we introduce a novel method that utilizes bistatic Doppler-rates estimated in GNSS-based multistatic radar configurations to estimate the velocity of ship targets with increased accuracy. We also put forward an advance passive multistatic imaging procedure by exploiting the target signature spread across multiple illumination angles in a GNSS-based multistatic radar system, resulting in higher image quality and improved classification capabilities.
Finally, we demonstrate the advantages of integrating shadow-based information obtained from multiple acquisitions in drone-borne SAR systems to enhance target feature extraction. Leveraging the high resolution achieved through drone-borne SAR imaging, along with the cost-effectiveness and ease of obtaining multiple images with such platforms, provides a comprehensive understanding of target morphology. This enables the extraction of critical details necessary for accurate classification.
The methodologies presented, supported by theoretical analysis, simulations, and experimental data, hold the promise of advancing radar technology and its applications across diverse scenarios.