This study explores the potential of Synthetic Aperture Radar (SAR) to aid Unmanned Aerial Vehicle (UAV) navigation when Inertial Navigation System (INS) measurements are not accurate enough to eliminate drifts from a planned trajectory. This problem can affect medium-altitude long-endurance (MALE) UAV class, which permits heavy and wide payloads (as required by SAR) and flights for thousands of kilometres accumulating large drifts. The basic idea is to infer position and attitude of an aerial platform by inspecting both amplitude and phase of SAR images acquired onboard. For the amplitude-based approach, the system navigation corrections are obtained by matching the actual coordinates of ground landmarks with those automatically extracted from the SAR image. When the use of SAR amplitude is unfeasible, the phase content can be exploited through SAR interferometry by using a reference Digital Terrain Model (DTM). A feasibility analysis was carried out to derive system requirements by exploring both radiometric and geometric parameters of the acquisition setting. We showed that MALE UAV, specific commercial navigation sensors and SAR systems, typical landmark position accuracy and classes, and available DTMs lead to estimate UAV coordinates with errors bounded within ±12 m, thus making feasible the proposed SAR-based backup system.

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In synthetic aperture radar (SAR) images, the speckle noise often corrupts salient information that is of interest (e.g. textures, small hard targets, object boundaries, etc.). To tackle such an issue, proposed is a novel anisotropic diffusion filter that manages to simultaneously fulfil competing requirements: noise reduction on homogeneous regions, weak edges preservation, and keeping hard targets intact. The capabilities of the proposed filter were proved by comparing it with another two non-linear diffusion filters applied on a Lena image corrupted by a multiplicative speckle noise and on a real SAR image acquired by the Cosmo-SkyMed satellite constellation.Introduction: In synthetic aperture radar (SAR) images, the speckle noise represents a key issue since it makes it difficult to interpret even simple acquired scenes containing few classes and objects. Presupposing a multiplicative speckle model, many despeckling filters were designed to remove such kinds of noise. For example, in the Gamma filter and in the more recent ones [1], knowledge of the data probability density function (PDF) is presupposed. Contrary to the aforementioned despeckling operators, the nonlinear diffusion (NLD) filters in [2] do not need any prior assumption, i.e. they can be directly applied to any type of noise-corrupted image. Nevertheless, little is known about the use of such filters for speckle removal and none of the filters described in [2] consider the edge and target preservation as a primary goal. In this Letter, a novel NLD denoising filter aimed at edge and target preservation for SAR images is devised. Moreover, to validate our approach, a comparison with NLD despeckling filters was performed on a Lena image corrupted by a multiplicative speckle noise and on a real 1-look Cosmo-SkyMed (CSK) image.