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List of Special Sessions of FUSION 2016 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 SS7 Space Object Detection, Tracking, Identification, and Classification SS8 Recent Advances in Estimation Performance Bounds and Applications SS9 Sequential Monte Carlo Methods for Complex Systems SS10 Multi-Level Fusion: bridging the gap between high and low level fusion

SS1 Intelligent Information Fusion 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

SS2 Dynamic Data Driven Application Systems for Sensor Data Problems 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

SS3 Context-based Information Fusion 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

SS4 Homotopy Methods for Progressive Bayesian Estimation 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

SS5 Data Fusion Methods for Indoor Localization of People and Objects 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

SS6 Directional Estimation 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

SS7 Space Object Detection, Tracking, Identification, and Classification Description: The operation of Earth-orbiting spacecraft has become increasingly difficult due to the proliferation of orbit debris and increased commercialization. This has been made evident by several collisions involving operational spacecraft in recent years. Maintaining sustainability of key orbit regimes, e.g., low-Earth, sun-synchronous, and geosynchronous orbits, requires improved tracking and prediction of up to hundreds of thousands of objects given sparse measurements in both space and time. Target identification and classification allows for better prediction and situational aware ness. Moreover, proper characterization of measurement assignments as well as the determination of measurement associations for maneuvering targets play a pivotal role in successful space situational awareness. Solutions to the problem will be interdisciplinary and require expertise in astrodynamics, computational sciences, information fusion, applied mathematics, and many other fields. The primary goal of this session is to promote interaction between the astrodynamics and space situational awareness community with those conducting research in information fusion and multi-target tracking. The secondary goal is a gathering of the individuals performing research on the associated topics to present, discuss, and disseminate ideas related to solving the detection, tracking, identification, and classification problems in the context of space situational awareness. Organizers: Kyle DeMars, Brandon Jones

SS8 Recent Advances in Estimation Performance Bounds and Applications Description: The field of estimation performance bounds has a long history. The perhaps most prominent example is the Cramer-Rao Lower bound (CRLB) which nowadays finds widespread use. Even though CRLB itself is established, there are many emerging areas, where it has not been evaluated. Besides the CRLB, there are other bounds that are often tighter, i.e. they better predict the estimation performance, such as the Barankin bound or Weiss-Weinstein bound, which are often more difficult to compute, but have recently attracted considerable interest in the research community. This special session aims at bringing together different experts in the field of estimation performance bounds to discuss the newest research results in this area. Of particular interest are developments of novel bounds, such as e.g. Bayesian bounds, non-Bayesian bounds, hybrid bounds, misspecified bounds, as well as new results for the CRLB with application to for instance target tracking, sensor networks, aerospace, or localization. Organizers: Carsten Fritsche and Fredrik Gustafsson

SS9 Sequential Monte Carlo Methods for Complex Systems Description: The aim of this special session is to address challenging problems such as estimation for high-dimensional systems and systems with complex dynamics (inter-relationships) with Sequential Monte Carlo (SMC) methods. This session will get together experts from different areas and is aimed at presenting novel techniques, algorithms, approaches especially based on sequential Monte Carlo methods. Both theoretically oriented and application related works are welcomed. Organizers: Lyudmila Mihaylova, Hans Driessen, Martin Ulmke, Fredrik Gustafsson, Fredrik Gunnarsson and Carsten Fritsche

SS10 Multi-Level Fusion: bridging the gap between high and low level fusion Description: The exploitation of all relevant information originating from a growing mass of heterogeneous sources, both device-based (sensors, video, etc.) and human-generated (text, voice, etc.), is a key factor for the production of timely, comprehensive and most accurate description of a situation or phenomenon. There is a growing need to effectively identify relevant information from the mass available, and exploit it through automatic fusion for timely, comprehensive and accurate situation awareness. Even if exploiting multiple sources, most fusion systems are developed for combing just one type of data (e.g. positional data) in order to achieve a certain goal (e.g. accurate target tracking) without considering other relevant information that could be of different origin, type, and with possibly very different representation (e.g. a priori knowledge, contextual knowledge, mission orders, risk maps, availability and coverage of sensing resources, etc.) but still very significant to augment the knowledge about observed entities. Very likely, this latter type of information could be considered of different fusion levels that rarely end up being systematically exploited automatically. The result is often stove-piped systems dedicated to a single fusion task with limited robustness. This is caused by the lack of an integrative approach for processing sensor data (low-level fusion) and semantically rich information (high-level fusion) in a holistic manner thus effectively implementing a multi-level processing architecture and fusion process. The proposed special session will bring together researchers working on fusion techniques and algorithms often considered to be at different and disjoint, fostering thus the discussion on the commonalities and differences in their research methodologies, and proposing viable multi-level fusion solutions to address challenging problems or relevant applications. Organizers: Lauro Snidaro, Jesus Garcia, Wolfgang Koch