Click here to download the call for special sessions.

As additional special sessions are announced, the title of each confirmed special session will be added to the topic list above in the paper submission interface. Authors submitting to a special session need to delay their submission until the special sessions are available.

• SS1 Intelligent Information Fusion
• SS2 Dynamic Data Driven Application Systems for Sensor Data Problems
• SS3 Context-based Information Fusion
• SS4 Homotopy Methods for Progressive Bayesian Estimation
• SS5 Data Fusion Methods for Indoor Localization of People and Objects
• SS6 Directional Estimation

Description: Research on Intelligent Systems for information fusion has matured during the last years and many effective applications of this technology are now deployed. The problem of Information Fusion has attracted significant attention in the artificial intelligence and machine learning community, trying to innovate in the techniques used for combining the data and to provide new models for estimations and predictions. The growing advances of Information Fusion accompanied with the advances of sensor technologies and distributed computing systems has led to new applications in different environments such as remote sensing, distributed surveillance, smart home care, network management etc. With the continuing expansion of the domain of interest and the increasing complexity of the collected information, intelligent techniques for fusion processing have become a crucial component in information fusion applications. In this sense, Intelligent systems can improve high level information fusion aimed at supporting decision making and/or intelligent information management.

Organizers: Juan Manuel Corchado, Javier Bajo, Tiancheng Li

Description: The Dynamic Data-Driven Application Systems (DDDAS) paradigm shapes a symbiotic feedback ecosystem consisting of models of physical and engineered systems and application instrumentation. Precisely, DDDAS establishes new avenues for accurate analysis and robust prediction, and control in application systems using multi-modal fusion of sensory data. The ubiquitous Big Data problems place the DDDAS as a unifying framework among applications, mathematical and statistical modeling, as well as information systems. Such challenges make the DDDAS paradigm now more relevant than ever that integrate modeling, measurements, and software. The DDDAS Session invites papers that demonstrate advances in the DDDAS paradigm that combine real-world applications, contemporary mathematical approaches, real-time large scale measurements, with software solutions. Key applications requiring DDDAS high-end computing solutions include distributed wireless platforms, distributed processing, collection and processing of sensor data for situation awareness, and critical infrastructure systems.

Organizers: Erik Blasch, Frederica Darema, Vasileios Maroulas, Ioannis D. Schizas

Description: The goal of the proposed session is discussing approaches to context-based information fusion. It will cover the design and development of information fusion solutions integrating sensory data with contextual knowledge. The context may be spread at different levels, with static or dynamic structure, and be represented in different ways, as maps, knowledge-bases, ontologies, etc. It can constitute a powerful tool to favour adaptability and systém performance. Therefore, the session covers both representation and exploitation mechanisms so that this knowledge can be efficiently integrated in the fusion process and enable adaptation mechanisms under different possible paradigms (intelligent systems, knowledge management, integration in fusion algorithms, etc). The applicability of advanced approaches can be illustrated with real-world applications of information fusion requiring a ontextualized approach.

Organizers: Jesus Garcia, Lauro Snidaro, José M. Molina, Ingrid Visentini

Description: This session is concerned with homotopy methods for the efficient solution of Bayesian state estimation problems occurring in information fusion and filtering. For state estimation in the presence of stochastic uncertainties, the best current estimate is represented by a probability density function. For that purpose, different representations are used including continuous densities such as Gaussian mixtures or discrete densities on continuous domain such as particle sets. Given prior knowledge in form of such a density, the goal is to include new information by means of Bayes' theorem. Typically, the resulting posterior density is of higher complexity and difficult to compute. In the case of particle sets, additional problems such as particle degeneracy occur. Hence, an appropriate approximate posterior has to be found. For recursive applications, this approximate posterior should be of the same form as the given prior density (approximate closedness). To cope with this challenging approximation problem, a well-established technique is to gradually include the new information instead of using it in one shot, which is achieved by a homotopy. For this session, manuscripts are invited that cover any aspect of homotopy methods for state estimation. This includes both theoretically oriented work and applications of known methods.

Organizers: Uwe D. Hanebeck, Fred Daum

Description: Indoor positioning has gained great importance as technology allows for affordable realtime sensing and processing systems. Researchers and developers can take advantage of the pervasiveness of WSNs (e.g., in the form of WLAN) and mobile sensors (such as smartphones) to obtain more accurate results by exploiting already existing infrastructure. Applications for indoor positioning include pedestrian navigation in public buildings and shops, location based services, safety for the elderly and impaired, museum guides, surveillance tasks, and also tracking products in manufacturing, warehousing, etc. Unlike outdoor environments, which are covered by GNSS to a satisfiable extent, indoor navigation faces additional challenges depending on the underlying measurement system such as occlusions, reflections and attenuation. While there are a great variety of sensors and measuring principles, in practice every single measuring technique suffers from deficits. While RF and (ultra-)sound are subject to multipath propagation, optical systems are intolerant to NLOS conditions. Some systems require setting up beacons, while others are self-calibrating and easy-to-install. Data fusion can overcome these limitations by combining complementary and redundant sensing techniques, with the application of algorithmic methods such as stochastic filtering. This Special Session addresses fundamental techniques, recent developments, and future research directions to help clear the way toward robust, accurate, indoor localization.

Organizers: Antonio Zea, Florian Faion, Uwe D. Hanebeck

Description: Many estimation problems of practical relevance include the problem of estimating directional quantities, for example angular values or orientations. However, conventional filters like the Kalman filter assume Gaussian distributions defined on Rn. This assumption neglects the inherent periodicity present in directional quantities. Consequently, more sophisticated approaches are required to accurately describe the circular setting. This Special Session addresses fundamental techniques, recent developments and future research directions in the field of estimation involving directional and periodic data. It is our goal to bridge the gap between theoreticians and practitioners. Thus, we welcome both applied and theoretic contributions on this topic.

Organizers: Gerhard Kurz, Igor Gilitschenski, Uwe D. Hanebeck