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

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

A notch filter for ship detection with polarimetric SAR data

Ship detection with Synthetic Aperture Radar (SAR) is a major topic for the security and monitoring of maritime areas. One of the advantages of using SAR lay in its capability to acquire useful images with any-weather conditions and at night time. Specifically, this paper proposes a new methodology exploiting polarimetric acquisitions (dual- and quad-polarimetric). The methodology adopted for the detector algorithm was introduced by the author and performs a perturbation analysis in space of polarimetric targets checking for coherence between the target to detect and its perturbed version on the data. In the present work, this methodology is optimized for detection of marine features. In the end, the algorithm can be considered to be a negative (notch) filter focused on sea. Consequently, all the features which have a polarimetric behavior different from the sea are detected (i.e. ships, icebergs, buoys, etc). Moreover, a dual polarimetric version of the detector is designed, to be exploited in the circumstances where quad polarimetric data cannot be acquired. The detector was tested with TerraSAR-X quad polarimetric data showing significant agreement with the available ground truth. Moreover, the theoretical performances of the detector are tested with Monte Carlo simulations in order to extract the probabilities of detection and false alarm. An important result is that the detector is, up to some extend, independent of the sea conditions

Adaptive multiscale detection of filamentary structures in a background of uniform random points

We are given a set of $n$ points that might be uniformly distributed in the unit square $[0,1]^2$. We wish to test whether the set, although mostly consisting of uniformly scattered points, also contains a small fraction of points sampled from some (a priori unknown) curve with $C^{\alpha}$-norm bounded by $\beta$. An asymptotic detection threshold exists in this problem; for a constant $T_-(\alpha,\beta)>0$, if the number of points sampled from the curve is smaller than $T_-(\alpha,\beta)n^{1/(1+\alpha)}$, reliable detection is not possible for large $n$. We describe a multiscale significant-runs algorithm that can reliably detect concentration of data near a smooth curve, without knowing the smoothness information $\alpha$ or $\beta$ in advance, provided that the number of points on the curve exceeds $T_*(\alpha,\beta)n^{1/(1+\alpha)}$. This algorithm therefore has an optimal detection threshold, up to a factor $T_*/T_-$. At the heart of our approach is an analysis of the data by counting membership in multiscale multianisotropic strips. The strips will have area $2/n$ and exhibit a variety of lengths, orientations and anisotropies. The strips are partitioned into anisotropy classes; each class is organized as a directed graph whose vertices all are strips of the same anisotropy and whose edges link such strips to their ``good continuations.'' The point-cloud data are reduced to counts that measure membership in strips. Each anisotropy graph is reduced to a subgraph that consist of strips with significant counts. The algorithm rejects $\mathbf{H}_0$ whenever some such subgraph contains a path that connects many consecutive significant counts.Comment: Published at http://dx.doi.org/10.1214/009053605000000787 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Priori Knowledge-Based Post-Doppler STAP for Traffic Monitoring with Airborne Radar

Die Verkehrsüberwachung gewinnt aufgrund des weltweiten Anstiegs der Verkehrsteilnehmer immer mehr an Bedeutung. Sicherer und effizierter Straßenverkehr erfordert detaillierte Verkehrsinformationen. Häufig sind diese lediglich stationär, räumlich stark begrenzt und meist nur auf Hauptverkehrsstraßen verfügbar. In dieser Hinsicht ist ein Ausfall des Telekommunikationsnetzes, beispielsweise im Falle einer Katastrophe, und der damit einhergehende Informationsverlust als kritisch einzustufen. Flugzeuggetragene Radarsysteme mit synthetischer Apertur (eng. Synthetic Aperture Radar - SAR) können für dieses Szenario eine Lösung darstellen, da sie großflächig hochauflösende Bilder generieren können, unabhängig von Tageslicht und Witterungsbedingungen. Sie ermöglichen aufgrund dieser Charakteristik die Detektion von Bewegtzielen am Boden (eng. ground moving target indication – GMTI). Moderne GMTI-Algorithmen und -Systeme, die prinzipiell für die Verkehrsüberwachung verwendbar sind, wurden in der Literatur bereits diskutiert. Allerdings ist die Robustheit dieser Systeme oft mit hohen Kosten, hoher Hardwarekomplexität und hohem Rechenaufwand verbunden. Diese Dissertation stellt einen neuartigen GMTI-Prozessor vor, der auf dem Radar-Mehrkanalverfahren post-Doppler space-time adaptive processing (PD STAP) basiert. Durch die Überlagerung einer Straßenkarte mit einem digitalen Höhenmodell ist es mithilfe des PD STAP möglich, Falschdetektionen zu erkennen und auszuschließen sowie die detektierten Fahrzeuge ihren korrekten Straßenpositionen zu zuordnen. Die präzisen Schätzungen von Position, Geschwindigkeit und Bewegungsrichtung der Fahrzeuge können mit vergleichsweise geringerer Hardware-Komplexität zu niedrigeren Kosten durchgeführt werden. Ferner wird im Rahmen dieser Arbeit ein effizienter Datenkalibrierungsalgorithmus erläutert, der das Ungleichgewicht zwischen den Empfangskanälen sowie die Variation des Dopplerschwerpunkts über Entfernung und Azimut korrigiert und so das Messergebnis verbessert. Darüber hinaus werden neue und automatisierte Strategien zur Erhebung von Trainingsdaten vorgestellt, die für die Schätzung der Clutter-Kovarianzmatrix wegen ihres direkten Einflusses auf die Clutter-Unterdrückung und Zieldetektion essentiell für PD STAP sind. Der neuartige PD STAP Prozessor verfügt über drei verschiedene Betriebsarten, die für militärische und zivile Anwendungen geeignet sind, darunter ein schneller Verarbeitungsalgorithmus der das Potential für eine zukünftige Echtzeit-Verkehrsüberwachung hat. Alle Betriebsarten wurden erfolgreich mit Radar-Mehrkanaldaten des flugzeuggetragenen F-SAR-Radarsensors des DLR getestet

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour