Target detection by means of space-based passive radar sensors generally requires the adoption of long integration time strategies to reinforce sufficiently the signal strength. These are usually based on the recovery of the target Doppler-rate to cope with the range and Doppler migration experienced over the long dwell considered. In this work, we put forward a Taylor-series approach that capitalizes on the set of bistatic Doppler-rates estimated in Global Navigation Satellite Systems (GNSS)-based multistatic radar configurations to estimate the velocity of ship targets with increased accuracy with respect to conventional Doppler-based procedures. Both the cases of single-element and array receiver configurations have been considered. Theoretical and numerical results under different use cases show as leveraging on the long integration times adopted at the detection stage could significantly increase the accuracy of the estimated ship velocity components. Few experimental results are also provided, verifying the potentialities of the proposed approach in operative scenarios of practical interest for this technology. The proposed approach is not limited at the GNSS case, but it could be potentially applied to any multistatic passive radar system.

This letter focuses on ship target localization using a multistatic passive radar system based on navigation satellites opportunistic illuminators. In such systems, the typical approach to localize the targets is taking peripheral decisions at each bistatic channel to detect the target followed by bistatic ranges intersection. In contrast, here we consider a centralized approach in order to jointly detect and localize the ship target of interest. The proposed approach entirely operates in a Cartesian plane representing the surveyed maritime area and it is able to combine the signals pertaining the different bistatic links despite the variation of the e.m. response of the targets, typically occurring in the framework under consideration as the target is illuminated by multiple (and possibly widely separated) transmitters viewing angles. Moreover, the hypothesis of point-like targets is here replaced with the more realistic case of extended targets (i.e., occupying more than one resolution cell). The ship localization effectiveness of the method is proved via both synthetic and experimental datasets.

This article puts forward a ship target detection and localisation technique for GNSS‐based passive multistatic radar. The approach capitalises on the large spatial diversity offered by navigation satellite constellations to provide maritime awareness using short integration time windows, thus bypassing the shortcomings suffered by approaches relying on long dwells typically considered in satellite‐based passive radar systems. The proposed approach entirely operates on the Cartesian plane and it is able to provide in a single stage the detection and localisation of the target in the surveyed area. Theoretical and simulated performance analysis are provided to illustrate as the proposed approach can outperform conventional two‐stages (i.e. bistatic detection then localisation) procedures, with particular regard to the case of significant radar cross section scintillation, likely experienced in the system under consideration. The effectiveness of the approach has been verified via experimental data acquired in a few scenarios of interest comprising opportunistic targets belonging to different types.

This paper deals with the exploitation of shadow regions in high-resolution drone-borne synthetic aperture radar (SAR) images and utilizing them for accurate target height and shape estimation. While prior research has shown the potential of SAR shadows for height estimation, these efforts have primarily focused on airborne and spaceborne platforms and lack a theoretical analysis for assessing achievable performance. In contrast, this work introduces a processing chain for target height and shape extraction and proposes a theoretical framework to evaluate system performance. The study demonstrates that the geometric flexibility and maneuverability of drones offer significant advantages in height estimation accuracy compared to conventional SAR platforms. The proposed method is validated using experimental data from a 24 GHz INRAS radar-equipped drone-borne SAR system, showing strong alignment between the extracted 2D shapes and height estimates and the theoretical predictions. Additionally, the study highlights how multi-perspective drone imagery enables the reconstruction of three-dimensional structures, further extending the potential of drone-borne SAR systems.

This paper proposes an inverse synthetic aperture radar (ISAR) technique specifically tailored for inland waterway surveillance using single-channel spaceborne SAR data. The approach delivers well-focused vessel images on a homogeneous plane, enabling reliable size estimation and supporting automatic target recognition. In addition, it jointly estimates vessel kinematic parameters, including speed and heading, and corrects azimuthal displacement to recover the actual vessel position. A key novelty lies in exploiting the elongated structural characteristics of ships for their positioning without relying on along-track interferometry, multi-platform acquisitions, or wake detection. Comprehensive theoretical and numerical performance analyses are presented under different use cases. The proposed method is further validated using real SAR data acquired over different waterways and vessel types, demonstrating robust performance and practical applicability across a wide range of operational scenarios relevant to inland traffic.

This work develops multistatic Inverse Synthetic Aperture Radar (ISAR) imaging techniques of ship targets using global navigation satellites signals (GNSS) as illuminators of opportunity and a single, ground-based receiver. This comprises an image scaling procedure to focus the data pertaining to different bistatic angles on a reference domain independent of the specific emitter, thus allowing a direct combination of images obtained under multiple and wide spatial separations. The procedure exploits the target state information made available by the very same spatial diversity offered by the system, also including the possibility of ship maneuvers during the image time. Moreover, to maximize the quality of the resulting image, an adaptive combination rule has been derived to preserve the highest information content from the individual bistatic data despite the variation of the target signature under the different perspectives. To explore the effectiveness of the technique under non-ideal conditions, theoretical and numerical performance analyses have been also provided considering errors in the target kinematic data. Moreover, the whole processing chain has been tested against experimental data comprising four Galileo satellites, showing the effectiveness of the proposed approach to achieve meaningful multistatic radar images of vessels at sea. The approach derived here is verified using GNSS-based radar, however it is generic and directly applicable to the broader class of passive radar imaging systems with satellite illuminators.