Situation assessment: an end-to-end process for the detection of objects of interest

International audienceIn this article, semi-automatic approaches are developed for wide area situation assessment in near-real-time. The two-step method consists of two granularity levels. The first entity assessment uses a new multi-target tracking algorithm (hybridization of GM-CPHD filter and MHT with road constraints) on GMTI data. The situation is then assessed by detecting objects of interest such as convoys with other data types (SAR, video). These detections are based on Bayesian networks and their credibilistic counterpart

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

Context Exploitation in Data Fusion

Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics. Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context. We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches

Improved nonlinear filtering for target tracking.

The objective of this research is to develop robust and accurate tracking algorithms for various tracking applications. These tracking problems can be formulated as nonlinear filtering problems. The tracking algorithms will be developed based on an emerging promising nonlinear filter technique, known as sequential importance sampling (nick-name: particle filtering). This technique was introduced to the engineering community in the early years of 2000, and it has recently drawn significant attention from engineers and researchers in a wide range of areas. Despite the encouraging results reported in the current literature, there are still many open questions to be answered. For the first time, the major research effort will be focusing on making improvement to the particle filter based tracking algorithm in the following three aspects: (I) refining the particle filtering process by designing better proposal distributions (II) refining the dynamic model by using multiple-model method, (i.e. using switching dynamics and jump Markov process) and (III) refining system measurements by incorporating a data fusion stage for multiple measurement cues

Situation Assessment for Advanced Driver Assistance Systems

Die Arbeit behandelt drei Fragestellungen: Zunächst liegt auf der Erarbeitung einer funktionalen Architekturdetaillierung für die Situationsanalyse. Hierfür wird ihre die Stellung der Situationsanalyse eines fortschrittlichen Fahrerassistenzsystems aufgezeigt, um im Anschluss die funktionale Architektur innerhalb der Situationsanalyse aufzuschlüsseln. Der zweite Teil der Arbeit beinhaltet die Vorstellung eines neuen Algorithmus zur Berechnung der Grenzen des kollisionsfrei erreichbaren Raumes. Er bildet den zentralen Kern der Situationsanalyse einer aktiven Gefahrenbremsung. Der Algorithmus zeichnet sich dadurch aus, dass er sowohl beliebig strukturierte statische Hindernisse als auch dynamische Verkehrsteilnehmer berücksichtigt. Zudem beachtet er deren Interaktionsbeziehungen und die Aufmerksamkeit des Fahrers. Im Zuge der Modellierung fließen auch neue Erkenntnisse über den Einfluss der Breite einer zu durchfahrenden Lücke ein, die aus einer Studie stammen. Kommt der Algorithmus zum Ergebnis, dass der kollisionsfrei erreichbare Raum vollständig durch Hindernisse begrenzt ist - es also keine Ausweichmöglichkeit gibt - so ist ein wesentliches Kriterium für eine automatische Notbremse erfüllt. Auf mehreren Präsentationen war diese echtzeitfähig implementierte Situationsanalyse elementarer Bestandteil der vorgestellten Technik, mit der unfallvermeidende Bremsungen bis in den Stillstand auch oberhalb von 60 km /h möglich werden. Der dritte Teil der Dissertation adressiert eine bislang noch ungelöste Herausforderung: Die Erkennung einer Einfädelsituation aus der Perspektive eines involvierten Fahrzeuges. Auf die Modellierung der Merkmale als auch des Klassifikators wird ausführlich eingegangen. Im Zuge dessen wird das im Forschungsbereich der Fahrerassistenz noch unbekannte Klassifikationsverfahren "'Scenario Based Random Forest"' vorgestellt und beurteilt. Abschließend kann gezeigt werden, dass der Erkennungsalgorithmus in 92% der Fälle zum richtigen Ergebnis führt.This work addresses three issues in the research area of situation analysis for advanced driver assistance systems. The focus of the first part is on the development of a detailed functional architecture for situation analysis. Therefore, the situation analysis' embedding in the overall system is shown. The functional architecture within the situation analysis is developed afterwards. The second part of this work introduces a new algorithm for calculating the borders of the collision-free reachability area. This algorithm forms the central piece of the situation analysis of an active hazard braking function. It considers arbitrary structured static obstacles, dynamic road users, their interaction relationships as well as the driver's state of attention. The influence of narrow gap's widths between obstacles in the context of evasion manoevers were addressed in a study. If the result of the computation indicates that the collision-free reachability area is completely limited by obstacles - in other words that no collision free track exists - an essential criterion for the use of an automatic emergency brake is met. The results were shown with a real vehicle using such an active hazard braking system. It demonstrated warrantable full stop collision avoiding braking interventions even at differential speeds above 60km/h. The third part of the thesis addresses the detection of convoy merging situations from the perspective of an involved vehicle. Within the development of such a classification algorithm, the method ``Scenario based random forest'' is introduced. This thesis describes the modeling of the situation and hence the features used for the classification as well as the training of the classifier in detail. The results show, that the convoy merging situations are classified correctly in 92% of given samples. All presented results were obtained by using real-world sensor data

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space