Geosynchronous synthetic aperture radar (GEO SAR) has been studied for several decades but has not yet been implemented. This paper provides an overview of mission design, describing significant constraints (atmosphere, orbit, temporal stability of the surface and atmosphere, measurement physics, and radar performance) and then uses these to propose an approach to initial system design. The methodology encompasses all GEO SAR mission concepts proposed to date. Important classifications of missions are: 1) those that require atmospheric phase compensation to achieve their design spatial resolution; and 2) those that achieve full spatial resolution without phase compensation. Means of estimating the atmospheric phase screen are noted, including a novel measurement of the mean rate of change of the atmospheric phase delay, which GEO SAR enables. Candidate mission concepts are described. It seems likely that GEO SAR will be feasible in a wide range of situations, although extreme weather and unstable surfaces (e.g., water, tall vegetation) prevent 100% coverage. GEO SAR offers an exciting imaging capability that powerfully complements existing systems.
Synthetic aperture radar (SAR) sensors represent one of the most effective means to support activities in the sector of maritime surveillance. In the field of ship detection, many SAR-based algorithms have been proposed recently, but none of them has ever considered the electromagnetic aspects behind the interactions of SAR signals with the ship and surrounding waters, with the detection step and rate strongly influenced by relative thresholding techniques applied to the SAR amplitude or intensity image. This paper introduces a novel model to evaluate the radar cross section (RCS) backscattered from a canonical ship adapted, to the case at issue, from similar existing models developed for, and applied to, urban areas. The RCS is modeled using the Kirchhoff approximation (KA) within the geometrical optics (GO) solution and, following some assumptions on the scene parameters, derived by empirical observations; its probability density function is derived for all polarizations. An analysis of the sensitiveness of the RCS to the uncertainty on the input scene parameters is then performed. The new model is validated on two different TerraSAR-X images acquired in November 2012 over the Solent area in the U.K.: the RCS relevant to several isolated ships is measured and compared with the expected value deriving from the theoretical model here introduced. Results are widely discussed and ranges of applicability finally suggested.
The paper shows a new algorithm for ship-detection from Synthetic Aperture Radar (SAR) images. The algorithm consists of three main stages: pre-processing, detection and discrimination. In the pre-processing a land mask is obtained considering the different statistics between the sea and the land's backscattered field; the detection stage isolates the bright points over the sea background employing a Constant False Alarm (CFAR) method; while the ships are retrieved, in the discrimination step, by evaluating the scattering contributions of the possible targets detected in the previous stage. The algorithm is tested on an airborne Sband SAR image of Portsmouth harbor, similar to those that will become available with the upcoming UK SAR mission NovaSAR-S.
This paper introduces a novel technique for ship-detection with Synthetic Aperture Radar (SAR) imagery based on the Generalized Likelihood Ratio Test (GLRT). Firstly, a suitable probability density function for a canonical ship is computed from the adoption of scattering models within the Geometrical Optic (GO) solution. Secondly, the GLRT is derived and the detector performance computed through Monte Carlo simulations. Finally, the GLRT technique is compared to the CFAR (Constant False Alarm Rate) algorithm in terms of ROC (Receiver Operating Characteristic) curves and computational load.
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