239 research outputs found

    New SAR Target Imaging Algorithm based on Oblique Projection for Clutter Reduction

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    International audienceWe have developed a new Synthetic Aperture Radar (SAR) algorithm based on physical models for the detection of a Man-Made Target (MMT) embedded in strong clutter (trunks in a forest). The physical models for the MMT and the clutter are represented by low-rank subspaces and are based on scattering and polarimetric properties. Our SAR algorithm applies the oblique projection of the received signal along the clutter subspace onto the target subspace. We compute its statistical performance in terms of probabilities of detection and false alarms. The performances of the proposed SAR algorithm are improved compared to those obtained with existing SAR algorithms: the MMT detection is greatly improved and the clutter is rejected. We also studied the robustness of our new SAR algorithm to interference modeling errors. Results on real FoPen (Foliage Penetration) data showed the usefulness of this approach

    An Approach to Ground Moving Target Indication Using Multiple Resolutions of Multilook Synthetic Aperture Radar Images

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    Ground moving target indication (GMTI) using multiple resolutions of synthetic aperture radar (SAR) images to estimate the clutter scattering statistics is shown to outperform conventional sample matrix inversion space-time adaptive processing GMTI techniques when jamming is not present. A SAR image provides an estimate of scattering from nonmoving targets in the form of a clutter scattering covariance matrix for the GMTI optimum processor. Since the homogeneity of the scattering statistics are unknown, using SAR images at multiple spatial resolutions to estimate the clutter scattering statistics results in more confidence in the final detection decision. Two approaches to calculating the multiple SAR resolutions are investigated. Multiple resolution filter bank smoothing of the full-resolution SAR image is shown to outperform an innovative approach to multilook SAR imaging. The multilook SAR images are calculated from a single measurement vector partitioned base on synthetic sensor locations determined via eigenanalysis of the radar measurement parameters

    Space/time/frequency methods in adaptive radar

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    Radar systems may be processed with various space, time and frequency techniques. Advanced radar systems are required to detect targets in the presence of jamming and clutter. This work studies the application of two types of radar systems. It is well known that targets moving along-track within a Synthetic Aperture Radar field of view are imaged as defocused objects. The SAR stripmap mode is tuned to stationary ground targets and the mismatch between the SAR processing parameters and the target motion parameters causes the energy to spill over to adjacent image pixels, thus hindering target feature extraction and reducing the probability of detection. The problem can be remedied by generating the image using a filter matched to the actual target motion parameters, effectively focusing the SAR image on the target. For a fixed rate of motion the target velocity can be estimated from the slope of the Doppler frequency characteristic. The problem is similar to the classical problem of estimating the instantaneous frequency of a linear FM signal (chirp). The Wigner-Ville distribution, the Gabor expansion, the Short-Time Fourier transform and the Continuous Wavelet Transform are compared with respect to their performance in noisy SAR data to estimate the instantaneous Doppler frequency of range compressed SAR data. It is shown that these methods exhibit sharp signal-to-noise threshold effects. The space-time radar problem is well suited to the application of techniques that take advantage of the low-rank property of the space-time covariance matrix. It is shown that reduced-rank methods outperform full-rank space-time adaptive processing when the space-time covariance matrix is estimated from a dataset with limited support. The utility of reduced-rank methods is demonstrated by theoretical analysis, simulations and analysis of real data. It is shown that reduced-rank processing has two effects on the performance: increased statistical stability which tends to improve performance, and introduction of a bias which lowers the signal-to-noise ratio. A method for evaluating the theoretical conditioned SNR for fixed reduced-rank transforms is also presented

    Spaceborne synthetic-aperture imaging radars: Applications, techniques, and technology

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    In the last four years, the first two Earth-orbiting, space-borne, synthetic-aperture imaging radars (SAR) were successfully developed and operated. This was a major achievement in the development of spaceborne radar sensors and ground processors. The data acquired with these sensors extended the capability of Earth resources and ocean-surface observation into a new region of the electromagnetic spectrum. This paper is a review of the different aspects of spaceborne imaging radars. It includes a review of: 1) the unique characteristics of space-borne SAR systems; 2) the state of the art in spaceborne SAR hardware and SAR optical and digital processors; 3) the different data-handling techniques; and 4) the different applications of spaceborne SAR data

    Design of large polyphase filters in the Quadratic Residue Number System

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    Synthèse des traitements STAP pour la détection en environnement hétérogène

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    Cet article synthétise les différents algorithmes spatio-temporels adaptatifs (STAP) développés et/ou utilisés pour la détection en environnement non-homogène. Nous rappelons en premier lieu les causes principales qui peuvent conduire à un environnement hétérogène. Puis nous présentons les stratégies STAP les plus communément utilisées dans de tels environnements

    Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

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    With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work
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