Strategies and Techniques for Use and Exploitation of Contextual Information in High-Level Fusion Architectures

Proceedings of: 13th Conference on Information Fusion (FUSION 2010): Edinburgh, UK. 26-29 July 2010.Contextual Information is proving to be not only an additional exploitable information source for improving entity and situational estimates in certain Information Fusion systems, but can also be the entire focus of estimation for such systems as those directed to Ambient Intelligence (AI) and Context-Aware(CA) applications. This paper will discuss the role(s) of Contextual Information (CI) in a wide variety of IF applications to include AI, CA, Defense, and Cyber-security among possible others, the issues involved in designing strategies and techniques for CI use and exploitation, provide some exemplars of evolving CI use/exploitation designs on our current projects, and describe some general frameworks that are evolving in various application domains where CI is proving critical.The UC3M Team gratefully acknowledge that this research activity is supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732- C02-02/TEC, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02. UC3M also thanks Prof. James Llinas for his helpful comments during his stay, which has been supported by the collaboration agreement ‘Chairs of Excellence’ between University Carlos III and Banco Santander. The US/UB Team gratefully acknowledge that this research activity is supported by a Multidisciplinary University Research Initiative (MURI) grant (Number W911NF-09-1-0392) for “Unified Research on Networkbased Hard/Soft Information Fusion”, issued by the US Army Research Office (ARO) under the program management of Dr. John LaveryPublicad

A Box Particle Filter for Stochastic and Set-theoretic Measurements with Association Uncertainty

This work develops a novel estimation approach for nonlinear dynamic stochastic systems by combining the sequential Monte Carlo method with interval analysis. Unlike the common pointwise measurements, the proposed solution is for problems with interval measurements with association uncertainty. The optimal theoretical solution can be formulated in the framework of random set theory as the Bernoulli filter for interval measurements. The straightforward particle filter implementation of the Bernoulli filter typically requires a huge number of particles since the posterior probability density function occupies a significant portion of the state space. In order to reduce the number of particles, without necessarily sacrificing estimation accuracy, the paper investigates an implementation based on box particles. A box particle occupies a small and controllable rectangular region of non-zero volume in the target state space. The numerical results demonstrate that the filter performs remarkably well: both target state and target presence are estimated reliably using a very small number of box particles

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Fusion of Sensor Data and Intelligence in FITS

Proceedings of: 16th International Conference on Information Fusion (FUSION 2013): Istambul, Turkey 9-12 July 2013.The design and implementation of fusion systems working in real conditions requires functional and performance specification, analysis of information input and contextual domain, and development of testing and validation tools. This paper presents a fusion system recently developed to operate with EW and ISR sensors on-board of patrol aircraft, which must be fused with information from other collaborative entities and intelligence in databases. The paper describes the overall organization of the system developed, modules and the data flow. The characterization of data sources and core algorithms for data alignment, uncertainty representation and fusion management are detailed and validated in realistic situations.This work was supported in part by Projects FITS-DFS (EADS/CASA), MEyC TEC2012-37832-C02-01, MEyC TEC2011-28626-C02-02 and CAM CONTEXTS (S2009/TIC-1485).Publicad

Contextual Knowledge and Information Fusion for Maritime Piracy Surveillance

Proceedings of: NATO Advanced Study Institute (ASI) on Prediction and Recognition of Piracy Efforts Using Collaborative Human-Centric Information Systems, Salamanca, 19-30 September, 2011Though piracy accounts for only a small fraction of the general losses of the maritime industry it creates a serious threat to the maritime security because of the connections between organized piracy and wider criminal networks and corruption on land. Fighting piracy requires monitoring the waterways, harbors,and criminal networks on the land to increase the ability of the decision makers to predict piracy attracts and manage operations to prevent or contain them. Piracy surveillance involves representing and processing huge amount heterogeneous information often uncertain, unreliable, and irrelevant within a specific context to detect and recognize suspicious activities to alert decision makers on vessel behaviors of interest with minimal false alarm. The paper discusses the role of information fusion, and context representation and utilization in building an piracy surveillance picture.This paper has utilized the results of the research activity supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC and CAM CONTEXTS (S2009/TIC-1485)Publicad

A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking

[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data. The first part of our review is on remarkable achievements that have been made for the single-target PF from several aspects including importance proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal systems. The second part of our review is on analyzing the intractable challenges raised within the general multitarget (multi-sensor) tracking due to random target birth and termination, false alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream multitarget PF approaches consist of two main classes, one based on M2T association approaches and the other not such as the finite set statistics-based PF. In either case, significant challenges remain due to unknown tracking scenarios and integrated tracking management