Scattering Center Extraction and Recognition Based on ESPRIT Algorithm

Inverse Synthetic Aperture Radar (ISAR) generates high quality radar images even in low visibility. And it provides important physical features for space target recognition and location. This thesis focuses on ISAR rapid imaging, scattering center information extraction, and target classification. Based on the principle of Fourier imaging, the backscattering field of radar target is obtained by physical optics (PO) algorithm, and the relation between scattering field and objective function is deduced. According to the resolution formula, the incident parameters of electromagnetic wave are set reasonably. The interpolation method is used to realize three-dimensional (3D) simulation of aircraft target, and the results are compared with direct imaging results. CLEAN algorithm extracts scattering center information effectively. But due to the limitation of resolution parameters, traditional imaging can’t meet the actual demand. Therefore, the super-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm is used to obtain spatial target location information. The signal subspace and noise subspace are orthogonal to each other. By combining spatial smoothing method with ESPRIT algorithm, the physical characteristics of geometric target scattering center are obtained accurately. In particular, the proposed method is validated on complex 3D aircraft targets and it proves that this method is applied to most scattering mechanisms. The distribution of scattering centers reflects the geometric information of the target. Therefore, the electromagnetic image to be recognized and ESPRIT image are matched by the domain matching method. And the classification results under different radii are obtained. In addition, because the neural network can extract rich image features, the improved ALEX network is used to classify and recognize target data processed by ESPRIT. It proves that ESPRIT algorithm can be used to expand the existing datasets and prepare for future identification of targets in real environments. Final a visual classification system is constructed to visually display the results

Maritime Moving Target Detection, Tracking and Geocoding Using Range-Compressed Airborne Radar Data

Eine regelmäßige und großflächige überwachung des Schiffsverkehrs gewinnt zunehmend an Bedeutung, vor allem auch um maritime Gefahrenlagen und illegale Aktivitäten rechtzeitig zu erkennen. Heutzutage werden dafür überwiegend das automatische Identifikationssystem (AIS) und stationäre Radarstationen an den Küsten eingesetzt. Luft- und weltraumgestützte Radarsensoren, die unabhängig vom Wetter und Tageslicht Daten liefern, können die vorgenannten Systeme sehr gut ergänzen. So können sie beispielsweise Schiffe detektieren, die nicht mit AIS-Transpondern ausgestattet sind oder die sich außerhalb der Reichweite der stationären AIS- und Radarstationen befinden. Luftgestützte Radarsensoren ermöglichen eine quasi-kontinuierliche Beobachtung von räumlich begrenzten Gebieten. Im Gegensatz dazu bieten weltraumgestützte Radare eine große räumliche Abdeckung, haben aber den Nachteil einer geringeren temporalen Abdeckung. In dieser Dissertation wird ein umfassendes Konzept für die Verarbeitung von Radardaten für die Schiffsverkehr-überwachung mit luftgestützten Radarsensoren vorgestellt. Die Hauptkomponenten dieses Konzepts sind die Detektion, das Tracking, die Geokodierung, die Bildgebung und die Fusion mit AIS-Daten. Im Rahmen der Dissertation wurden neuartige Algorithmen für die ersten drei Komponenten entwickelt. Die Algorithmen sind so aufgebaut, dass sie sich prinzipiell für zukünftige Echtzeitanwendungen eignen, die eine Verarbeitung an Bord der Radarplattform erfordern. Darüber hinaus eignen sich die Algorithmen auch für beliebige, nicht-lineare Flugpfade der Radarplattform. Sie sind auch robust gegenüber Lagewinkeländerungen, die während der Datenerfassung aufgrund von Luftturbulenzen jederzeit auftreten können. Die für die Untersuchungen verwendeten Daten sind ausschließlich entfernungskomprimierte Radardaten. Da das Signal-Rausch-Verhältnis von Flugzeugradar-Daten im Allgemeinen sehr hoch ist, benötigen die neuentwickelten Algorithmen keine vollständig fokussierten Radarbilder. Dies reduziert die Gesamtverarbeitungszeit erheblich und ebnet den Weg für zukünftige Echtzeitanwendungen. Der entwickelte neuartige Schiffsdetektor arbeitet direkt im Entfernungs-Doppler-Bereich mit sehr kurzen kohärenten Verarbeitungsintervallen (CPIs) der entfernungskomprimierten Radardaten. Aufgrund der sehr kurzen CPIs werden die detektierten Ziele im Dopplerbereich fokussiert abgebildet. Wenn sich die Schiffe zusätzlich mit einer bestimmten Radialgeschwindigkeit bewegen, werden ihre Signale aus dem Clutter-Bereich hinausgeschoben. Dies erhöht das Verhältnis von Signal- zu Clutter-Energie und verbessert somit die Detektierbarkeit. Die Genauigkeit der Detektion hängt stark von der Qualität der von der Meeresoberfläche rückgestreuten Radardaten ab, die für die Schätzung der Clutter-Statistik verwendet werden. Diese wird benötigt, um einen Detektions-Schwellenwert für eine konstante Fehlalarmrate (CFAR) abzuleiten und die Anzahl der Fehlalarme niedrig zu halten. Daher umfasst der vorgeschlagene Detektor auch eine neuartige Methode zur automatischen Extraktion von Trainingsdaten für die Statistikschätzung sowie geeignete Ozean-Clutter-Modelle. Da es sich bei Schiffen um ausgedehnte Ziele handelt, die in hochauflösenden Radardaten mehr als eine Auflösungszelle belegen, werden nach der Detektion mehrere von einem Ziel stammende Pixel zu einem physischen Objekten zusammengefasst, das dann in aufeinanderfolgenden CPIs mit Hilfe eines Bewegungsmodells und eines neuen Mehrzielverfolgungs-Algorithmus (Multi-Target Tracking) getrackt wird. Während des Trackings werden falsche Zielspuren und Geisterzielspuren automatisch erkannt und durch ein leistungsfähiges datenbankbasiertes Track-Management-System terminiert. Die Zielspuren im Entfernungs-Doppler-Bereich werden geokodiert bzw. auf den Boden projiziert, nachdem die Einfallswinkel (DOA) aller Track-Punkte geschätzt wurden. Es werden verschiedene Methoden zur Schätzung der DOA-Winkel für ausgedehnte Ziele vorgeschlagen und anhand von echten Radardaten, die Signale von echten Schiffen beinhalten, bewertet

Three Dimensional Inverse Synthetic Aperture Radar Imaging

This research investigates the generation, display, and interpretation of three-dimensional (3-D) Synthetic Aperture Radar images. Three-dimensional assumes that the data collected consists of one temporal dimension and two orthogonal angular dimensions. From this data, a three dimensional reflectivity map, or 3-D image, of a target can be constructed. This thesis effort develops and applies a three-dimensional imaging algorithm on actual radar data measured on a one-third scale model of a C-29 aircraft. Two-dimensional slices of the three-dimensional image as well as three-dimensional isosurfaces are compared to the physical properties of the target. The results demonstrate the ability to produce three-dimensional images given three-dimensional radar data

Moving Target Analysis in ISAR Image Sequences with a Multiframe Marked Point Process Model

In this paper we propose a Multiframe Marked Point Process model of line segments and point groups for automatic target structure extraction and tracking in Inverse Synthetic Aperture Radar (ISAR) image sequences. For the purpose of dealing with scatterer scintillations and high speckle noise in the ISAR frames, we obtain the resulting target sequence by an iterative optimization process, which simultaneously considers the observed image data and various prior geometric interaction constraints between the target appearances in the consecutive frames. A detailed quantitative evaluation is performed on 8 real ISAR image sequences of different carrier ship and airplane targets, using a test database containing 545 manually annotated frames

A sparsity-driven approach for joint SAR imaging and phase error correction

Image formation algorithms in a variety of applications have explicit or implicit dependence on a mathematical model of the observation process. Inaccuracies in the observation model may cause various degradations and artifacts in the reconstructed images. The application of interest in this paper is synthetic aperture radar (SAR) imaging, which particularly suffers from motion-induced model errors. These types of errors result in phase errors in SAR data which cause defocusing of the reconstructed images. Particularly focusing on imaging of fields that admit a sparse representation, we propose a sparsity-driven method for joint SAR imaging and phase error correction. Phase error correction is performed during the image formation process. The problem is set up as an optimization problem in a nonquadratic regularization-based framework. The method involves an iterative algorithm each iteration of which consists of consecutive steps of image formation and model error correction. Experimental results show the effectiveness of the approach for various types of phase errors, as well as the improvements it provides over existing techniques for model error compensation in SAR

Advanced studies of electromagnetic scattering

In radar signature applications it is often desirable to generate the range profiles and inverse synthetic aperture radar (ISAR) images of a target. They can be used either as identification tools to distinguish and classify the target from a collection of possible targets, or as diagnostic/design tools to pinpoint the key scattering centers on the target. The simulation of synthetic range profiles and ISAR images is usually a time intensive task and computation time is of prime importance. Our research has been focused on the development of fast simulation algorithms for range profiles and ISAR images using the shooting and bouncing ray (SBR) method, a high frequency electromagnetic simulation technique for predicting the radar returns from realistic aerospace vehicles and the scattering by complex media

Ship detection on open sea and coastal environment

Synthetic Aperture Radar (SAR) is a high-resolution ground-mapping technique with the ability to effectively synthesize a large radar antenna by processing the phase of a smaller radar antenna on a moving platform like an airplane or a satellite. SAR images, due to its properties, have been the focus of many applications such as land and sea monitoring, remote sensing, mapping of surfaces, weather forecasting, among many others. Their relevance is increasing on a daily basis, thus it’s crucial to apply the best suitable method or technique to each type of data collected. Several techniques have been published in the literature so far to enhance automatic ship detection using Synthetic Aperture Radar (SAR) images, like multilook imaging techniques, polarization techniques, Constant False Alarm Rate (CFAR) techniques, Amplitude Change Detection (ACD) techniques among many others. Depending on how the information is gathered and processed, each technique presents different performance and results. Nowadays there are several ongoing SAR missions, and the need to improve ship detection, oil-spills or any kind of sea activity is fundamental to preserve and promote navigation safety as well as constant and accurate monitoring of the surroundings, for example, detection of illegal fishing activities, pollution or drug trafficking. The main objective of this MSc dissertation is to study and implement a set of algorithms for automatic ship detection using SAR images from Sentinel-1 due to its characteristics as well as its ease access. The dissertation organization is as follows: Chapter 1 presents a brief introduction to the theme of this dissertation and its aim, as well as its structure; Chapter 2 summarizes a variety of fundamental key points from historical events and developments to the SAR theory, finishing with a summary of some well-known ship detection methods; Chapter 3 presents a basic guideline to choose the best ship detection technique depending on the data type and operational scenario; Chapter 4 focus on the CFAR technique detailing the implemented algorithms. This technique was selected, given the data set available for testing in this work; Chapter 5 presents the results obtained using the implemented algorithms; Chapter 6 presents the conclusions, final remarks and future work

Multichannel techniques for 3D ISAR

This thesis deals with the challenge of forming 3D target reconstruction by using spatial multi-channel ISAR configurations. The standard output of an ISAR imaging system is a 2D projection of the true three-dimensional target reflectivity onto an image plane. The orientation of the image plane cannot be predicted a priori as it strongly depends on the radar-target geometry and on the target motion, which is typically unknown. This leads to a difficult interpretation of the ISAR images. In this scenario, this thesis aim to give possible solutions to such problems by proposing three 3D processing based on interferometry, beamforming techniques and MIMO InISAR systems. The CLEAN method for scattering centres extraction is extended to multichannel ISAR systems and a multistatic 3D target reconstruction that is based on a incoherent technique is suggested