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Maritime data integration and analysis: Recent progress and research challenges
The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems
Application of track-before-detect techniques in GNSS-based passive radar for maritime surveillance
GNSS-based passive radar has been recently proved able to enable moving target detection in maritime surveillance applications. The main restriction lies in the low Equivalent Isotropic Radiated Power (EIRP) level of navigation satellites. Extending the integration times with proper target motion compensation has been shown to be a viable solution to improve ship detectability, but this involves computational complexity and increasing sensitivity to motion model mismatches. In this work, we consider the application of a Track-Before-Detect (TBD) method to considerably increase the integration time (and therefore the detection capability) at the same time keeping the computational complexity affordable by practical systems. Dynamic programming TBD algorithms have been specialized for the considered framework and tested against experimental dataset. The obtained results show the effectiveness of this approach to improve the detection capability of the system despite the restricted power budget
Adaptive Douglas-Peucker Algorithm With Automatic Thresholding for AIS-Based Vessel Trajectory Compression
Automatic identification system (AIS) is an important part of perfecting terrestrial networks, radar systems and satellite constellations. It has been widely used in vessel traffic service system to improve navigational safety. Following the explosion in vessel AIS data, the issues of data storing, processing, and analysis arise as emerging research topics in recent years. Vessel trajectory compression is used to eliminate the redundant information, preserve the key features, and simplify information for further data mining, thus correspondingly improving data quality and guaranteeing accurate measurement for ensuring navigational safety. It is well known that trajectory compression quality significantly depends on the threshold selection. We propose an Adaptive Douglas-Peucker (ADP) algorithm with automatic thresholding for AIS-based vessel trajectory compression. In particular, the optimal threshold is adaptively calculated using a novel automatic threshold selection method for each trajectory, as an improvement and complement of original Douglas-Peucker (DP) algorithm. It is developed based on the channel and trajectory characteristics, segmentation framework, and mean distance. The proposed method is able to simplify vessel trajectory data and extract useful information effectively. The time series trajectory classification and clustering are discussed and analysed based on ADP algorithm in this paper. To verify the reasonability and effectiveness of the proposed method, experiments are conducted on two different trajectory data sets in inland waterway of Yangtze River for trajectory classification based on the nearest neighbor classifier, and for trajectory clustering based on the spectral clustering. Comprehensive results demonstrate that the proposed algorithm can reduce the computational cost while ensuring the clustering and classification accuracy
Maritime Moving Target Detection, Tracking and Geocoding Using Range-Compressed Airborne Radar Data
Eine regelmĂ€Ăige und groĂflĂ€chige ĂŒberwachung des Schiffsverkehrs gewinnt zunehmend an Bedeutung, vor allem auch um maritime Gefahrenlagen und illegale AktivitĂ€ten rechtzeitig zu erkennen. Heutzutage werden dafĂŒr ĂŒberwiegend das automatische Identifikationssystem (AIS) und stationĂ€re Radarstationen an den KĂŒsten eingesetzt. Luft- und weltraumgestĂŒtzte Radarsensoren, die unabhĂ€ngig vom Wetter und Tageslicht Daten liefern, können die vorgenannten Systeme sehr gut ergĂ€nzen. So können sie beispielsweise Schiffe detektieren, die nicht mit AIS-Transpondern ausgestattet sind oder die sich auĂerhalb der Reichweite der stationĂ€ren AIS- und Radarstationen befinden. LuftgestĂŒtzte Radarsensoren ermöglichen eine quasi-kontinuierliche Beobachtung von rĂ€umlich begrenzten Gebieten. Im Gegensatz dazu bieten weltraumgestĂŒtzte Radare eine groĂe rĂ€umliche Abdeckung, haben aber den Nachteil einer geringeren temporalen Abdeckung.
In dieser Dissertation wird ein umfassendes Konzept fĂŒr die Verarbeitung von Radardaten fĂŒr die Schiffsverkehr-ĂŒberwachung mit luftgestĂŒtzten 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 fĂŒr die ersten drei Komponenten entwickelt. Die Algorithmen sind so aufgebaut, dass sie sich prinzipiell fĂŒr zukĂŒnftige Echtzeitanwendungen eignen, die eine Verarbeitung an Bord der Radarplattform erfordern. DarĂŒber hinaus eignen sich die Algorithmen auch fĂŒr beliebige, nicht-lineare Flugpfade der Radarplattform. Sie sind auch robust gegenĂŒber LagewinkelĂ€nderungen, die wĂ€hrend der Datenerfassung aufgrund von Luftturbulenzen jederzeit auftreten können.
Die fĂŒr die Untersuchungen verwendeten Daten sind ausschlieĂlich entfernungskomprimierte Radardaten. Da das Signal-Rausch-VerhĂ€ltnis von Flugzeugradar-Daten im Allgemeinen sehr hoch ist, benötigen die neuentwickelten Algorithmen keine vollstĂ€ndig fokussierten Radarbilder. Dies reduziert die Gesamtverarbeitungszeit erheblich und ebnet den Weg fĂŒr zukĂŒnftige Echtzeitanwendungen.
Der entwickelte neuartige Schiffsdetektor arbeitet direkt im Entfernungs-Doppler-Bereich mit sehr kurzen kohĂ€renten Verarbeitungsintervallen (CPIs) der entfernungskomprimierten Radardaten. Aufgrund der sehr kurzen CPIs werden die detektierten Ziele im Dopplerbereich fokussiert abgebildet. Wenn sich die Schiffe zusĂ€tzlich mit einer bestimmten Radialgeschwindigkeit bewegen, werden ihre Signale aus dem Clutter-Bereich hinausgeschoben. Dies erhöht das VerhĂ€ltnis von Signal- zu Clutter-Energie und verbessert somit die Detektierbarkeit. Die Genauigkeit der Detektion hĂ€ngt stark von der QualitĂ€t der von der MeeresoberflĂ€che rĂŒckgestreuten Radardaten ab, die fĂŒr die SchĂ€tzung der Clutter-Statistik verwendet werden. Diese wird benötigt, um einen Detektions-Schwellenwert fĂŒr 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 fĂŒr die StatistikschĂ€tzung 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. WÀhrend des Trackings werden falsche Zielspuren und Geisterzielspuren automatisch erkannt und durch ein leistungsfÀhiges 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 geschĂ€tzt wurden. Es werden verschiedene Methoden zur SchĂ€tzung der DOA-Winkel fĂŒr ausgedehnte Ziele vorgeschlagen und anhand von echten Radardaten, die Signale von echten Schiffen beinhalten, bewertet
Wireless Sensor Networks for Ecosystem Monitoring & Port Surveillance
International audienceProviding a wide variety of the most up - to - date innovations in sensor technology and sensor networks, our current project should achieve two major goals. The first goal covers various issues related to the public maritime transport safety and security, such as the coastal and port surveillance systems. While the second one w ill improve the capacity of public authorities to develop and implement smart environment policies by monitoring the shallow coastal water ecosystems. At this stage of our project, a surveillance platform has been already installed near the "MolĂšne Island" which is a small but the largest island of an archipelago of many islands located off the West coast of Brittany in North Western France. Our final objective is to add various sensors as well as to design, develop and implement new algorithms to extend th e capacity of the existing platform and reach the goals of our project. Finally, this manuscript introduces the identified approaches as well as t he second phase of the project which consists in analyzing living underwater micro - organisms (the population o f Marine Micro - Organisms, i.e. MMOs such as Phytoplankton and Zooplankton micro - zooplankton, but also heterotrophic bacterioplankton) in order to predict the health conditions of the macro - environment s . In addition, this communication discusses developed t echniques and concepts to deal with several practical problems related to our project. Some results are given and the whole system architecture is briefly described. This manuscript will also addresses the national benefit of such projects in the case of t hree different countries (Australia, France and KS
2015 Oil Observing Tools: A Workshop Report
Since 2010, the National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) have provided satellite-based pollution surveillance in United States waters to regulatory agencies such as the United States Coast Guard (USCG). These technologies provide agencies with useful information regarding possible oil discharges. Unfortunately, there has been confusion as to how to interpret the images collected by these satellites and other aerial platforms, which can generate misunderstandings during spill events. Remote sensor packages on aircraft and satellites have advantages and disadvantages vis-Ă -vis human observers, because they do not âseeâ features or surface oil the same way. In order to improve observation capabilities during oil spills, applicable technologies must be identified, and then evaluated with respect to their advantages and disadvantages for the incident. In addition, differences between sensors (e.g., visual, IR, multispectral sensors, radar) and platform packages (e.g., manned/unmanned aircraft, satellites) must be understood so that reasonable approaches can be made if applicable and then any data must be correctly interpreted for decision support. NOAA convened an Oil Observing Tools Workshop to focus on the above actions and identify training gaps for oil spill observers and remote sensing interpretation to improve future oil surveillance, observation, and mapping during spills. The Coastal Response Research Center (CRRC) assisted NOAAâs Office of Response and Restoration (ORR) with this effort. The workshop was held on October 20-22, 2015 at NOAAâs Gulf of Mexico Disaster Response Center in Mobile, AL. The expected outcome of the workshop was an improved understanding, and greater use of technology to map and assess oil slicks during actual spill events. Specific workshop objectives included:
âąIdentify new developments in oil observing technologies useful for real-time (or near real-time) mapping of spilled oil during emergency events.
âąIdentify merits and limitations of current technologies and their usefulness to emergency response mapping of oil and reliable prediction of oil surface transport and trajectory forecasts.Current technologies include: the traditional human aerial observer, unmanned aircraft surveillance systems, aircraft with specialized senor packages, and satellite earth observing systems.
âąAssess training needs for visual observation (human observers with cameras) and sensor technologies (including satellites) to build skills and enhance proper interpretation for decision support during actual events
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